WorldWideScience

Sample records for distributed forecasting model

  1. Wind and load forecast error model for multiple geographically distributed forecasts

    Energy Technology Data Exchange (ETDEWEB)

    Makarov, Yuri V.; Reyes-Spindola, Jorge F.; Samaan, Nader; Diao, Ruisheng; Hafen, Ryan P. [Pacific Northwest National Laboratory, Richland, WA (United States)

    2010-07-01

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

  2. Distributed HUC-based modeling with SUMMA for ensemble streamflow forecasting over large regional domains.

    Science.gov (United States)

    Saharia, M.; Wood, A.; Clark, M. P.; Bennett, A.; Nijssen, B.; Clark, E.; Newman, A. J.

    2017-12-01

    Most operational streamflow forecasting systems rely on a forecaster-in-the-loop approach in which some parts of the forecast workflow require an experienced human forecaster. But this approach faces challenges surrounding process reproducibility, hindcasting capability, and extension to large domains. The operational hydrologic community is increasingly moving towards `over-the-loop' (completely automated) large-domain simulations yet recent developments indicate a widespread lack of community knowledge about the strengths and weaknesses of such systems for forecasting. A realistic representation of land surface hydrologic processes is a critical element for improving forecasts, but often comes at the substantial cost of forecast system agility and efficiency. While popular grid-based models support the distributed representation of land surface processes, intermediate-scale Hydrologic Unit Code (HUC)-based modeling could provide a more efficient and process-aligned spatial discretization, reducing the need for tradeoffs between model complexity and critical forecasting requirements such as ensemble methods and comprehensive model calibration. The National Center for Atmospheric Research is collaborating with the University of Washington, the Bureau of Reclamation and the USACE to implement, assess, and demonstrate real-time, over-the-loop distributed streamflow forecasting for several large western US river basins and regions. In this presentation, we present early results from short to medium range hydrologic and streamflow forecasts for the Pacific Northwest (PNW). We employ a real-time 1/16th degree daily ensemble model forcings as well as downscaled Global Ensemble Forecasting System (GEFS) meteorological forecasts. These datasets drive an intermediate-scale configuration of the Structure for Unifying Multiple Modeling Alternatives (SUMMA) model, which represents the PNW using over 11,700 HUCs. The system produces not only streamflow forecasts (using the Mizu

  3. Real-Time Analysis and Forecasting of Multisite River Flow Using a Distributed Hydrological Model

    Directory of Open Access Journals (Sweden)

    Mingdong Sun

    2014-01-01

    Full Text Available A spatial distributed hydrological forecasting system was developed to promote the analysis of river flow dynamic state in a large basin. The research presented the real-time analysis and forecasting of multisite river flow in the Nakdong River Basin using a distributed hydrological model with radar rainfall forecast data. A real-time calibration algorithm of hydrological distributed model was proposed to investigate the particular relationship between the water storage and basin discharge. Demonstrate the approach of simulating multisite river flow using a distributed hydrological model couple with real-time calibration and forecasting of multisite river flow with radar rainfall forecasts data. The hydrographs and results exhibit that calibrated flow simulations are very approximate to the flow observation at all sites and the accuracy of forecasting flow is gradually decreased with lead times extending from 1 hr to 3 hrs. The flow forecasts are lower than the flow observation which is likely caused by the low estimation of radar rainfall forecasts. The research has well demonstrated that the distributed hydrological model is readily applicable for multisite real-time river flow analysis and forecasting in a large basin.

  4. Palm oil price forecasting model: An autoregressive distributed lag (ARDL) approach

    Science.gov (United States)

    Hamid, Mohd Fahmi Abdul; Shabri, Ani

    2017-05-01

    Palm oil price fluctuated without any clear trend or cyclical pattern in the last few decades. The instability of food commodities price causes it to change rapidly over time. This paper attempts to develop Autoregressive Distributed Lag (ARDL) model in modeling and forecasting the price of palm oil. In order to use ARDL as a forecasting model, this paper modifies the data structure where we only consider lagged explanatory variables to explain the variation in palm oil price. We then compare the performance of this ARDL model with a benchmark model namely ARIMA in term of their comparative forecasting accuracy. This paper also utilize ARDL bound testing approach to co-integration in examining the short run and long run relationship between palm oil price and its determinant; production, stock, and price of soybean as the substitute of palm oil and price of crude oil. The comparative forecasting accuracy suggests that ARDL model has a better forecasting accuracy compared to ARIMA.

  5. Incorporating spatial autocorrelation into species distribution models alters forecasts of climate-mediated range shifts.

    Science.gov (United States)

    Crase, Beth; Liedloff, Adam; Vesk, Peter A; Fukuda, Yusuke; Wintle, Brendan A

    2014-08-01

    Species distribution models (SDMs) are widely used to forecast changes in the spatial distributions of species and communities in response to climate change. However, spatial autocorrelation (SA) is rarely accounted for in these models, despite its ubiquity in broad-scale ecological data. While spatial autocorrelation in model residuals is known to result in biased parameter estimates and the inflation of type I errors, the influence of unmodeled SA on species' range forecasts is poorly understood. Here we quantify how accounting for SA in SDMs influences the magnitude of range shift forecasts produced by SDMs for multiple climate change scenarios. SDMs were fitted to simulated data with a known autocorrelation structure, and to field observations of three mangrove communities from northern Australia displaying strong spatial autocorrelation. Three modeling approaches were implemented: environment-only models (most frequently applied in species' range forecasts), and two approaches that incorporate SA; autologistic models and residuals autocovariate (RAC) models. Differences in forecasts among modeling approaches and climate scenarios were quantified. While all model predictions at the current time closely matched that of the actual current distribution of the mangrove communities, under the climate change scenarios environment-only models forecast substantially greater range shifts than models incorporating SA. Furthermore, the magnitude of these differences intensified with increasing increments of climate change across the scenarios. When models do not account for SA, forecasts of species' range shifts indicate more extreme impacts of climate change, compared to models that explicitly account for SA. Therefore, where biological or population processes induce substantial autocorrelation in the distribution of organisms, and this is not modeled, model predictions will be inaccurate. These results have global importance for conservation efforts as inaccurate

  6. Large-watershed flood simulation and forecasting based on different-resolution distributed hydrological model

    Science.gov (United States)

    Li, J.

    2017-12-01

    Large-watershed flood simulation and forecasting is very important for a distributed hydrological model in the application. There are some challenges including the model's spatial resolution effect, model performance and accuracy and so on. To cope with the challenge of the model's spatial resolution effect, different model resolution including 1000m*1000m, 600m*600m, 500m*500m, 400m*400m, 200m*200m were used to build the distributed hydrological model—Liuxihe model respectively. The purpose is to find which one is the best resolution for Liuxihe model in Large-watershed flood simulation and forecasting. This study sets up a physically based distributed hydrological model for flood forecasting of the Liujiang River basin in south China. Terrain data digital elevation model (DEM), soil type and land use type are downloaded from the website freely. The model parameters are optimized by using an improved Particle Swarm Optimization(PSO) algorithm; And parameter optimization could reduce the parameter uncertainty that exists for physically deriving model parameters. The different model resolution (200m*200m—1000m*1000m ) are proposed for modeling the Liujiang River basin flood with the Liuxihe model in this study. The best model's spatial resolution effect for flood simulation and forecasting is 200m*200m.And with the model's spatial resolution reduction, the model performance and accuracy also become worse and worse. When the model resolution is 1000m*1000m, the flood simulation and forecasting result is the worst, also the river channel divided based on this resolution is differs from the actual one. To keep the model with an acceptable performance, minimum model spatial resolution is needed. The suggested threshold model spatial resolution for modeling the Liujiang River basin flood is a 500m*500m grid cell, but the model spatial resolution with a 200m*200m grid cell is recommended in this study to keep the model at a best performance.

  7. Robust Hydrological Forecasting for High-resolution Distributed Models Using a Unified Data Assimilation Approach

    Science.gov (United States)

    Hernandez, F.; Liang, X.

    2017-12-01

    Reliable real-time hydrological forecasting, to predict important phenomena such as floods, is invaluable to the society. However, modern high-resolution distributed models have faced challenges when dealing with uncertainties that are caused by the large number of parameters and initial state estimations involved. Therefore, to rely on these high-resolution models for critical real-time forecast applications, considerable improvements on the parameter and initial state estimation techniques must be made. In this work we present a unified data assimilation algorithm called Optimized PareTo Inverse Modeling through Inverse STochastic Search (OPTIMISTS) to deal with the challenge of having robust flood forecasting for high-resolution distributed models. This new algorithm combines the advantages of particle filters and variational methods in a unique way to overcome their individual weaknesses. The analysis of candidate particles compares model results with observations in a flexible time frame, and a multi-objective approach is proposed which attempts to simultaneously minimize differences with the observations and departures from the background states by using both Bayesian sampling and non-convex evolutionary optimization. Moreover, the resulting Pareto front is given a probabilistic interpretation through kernel density estimation to create a non-Gaussian distribution of the states. OPTIMISTS was tested on a low-resolution distributed land surface model using VIC (Variable Infiltration Capacity) and on a high-resolution distributed hydrological model using the DHSVM (Distributed Hydrology Soil Vegetation Model). In the tests streamflow observations are assimilated. OPTIMISTS was also compared with a traditional particle filter and a variational method. Results show that our method can reliably produce adequate forecasts and that it is able to outperform those resulting from assimilating the observations using a particle filter or an evolutionary 4D variational

  8. Multi-Model Prediction for Demand Forecast in Water Distribution Networks

    Directory of Open Access Journals (Sweden)

    Rodrigo Lopez Farias

    2018-03-01

    Full Text Available This paper presents a multi-model predictor called Qualitative Multi-Model Predictor Plus (QMMP+ for demand forecast in water distribution networks. QMMP+ is based on the decomposition of the quantitative and qualitative information of the time-series. The quantitative component (i.e., the daily consumption prediction is forecasted and the pattern mode estimated using a Nearest Neighbor (NN classifier and a Calendar. The patterns are updated via a simple Moving Average scheme. The NN classifier and the Calendar are executed simultaneously every period and the most suited model for prediction is selected using a probabilistic approach. The proposed solution for water demand forecast is compared against Radial Basis Function Artificial Neural Networks (RBF-ANN, the statistical Autoregressive Integrated Moving Average (ARIMA, and Double Seasonal Holt-Winters (DSHW approaches, providing the best results when applied to real demand of the Barcelona Water Distribution Network. QMMP+ has demonstrated that the special modelling treatment of water consumption patterns improves the forecasting accuracy.

  9. Forecasting economy with Bayesian autoregressive distributed lag model: choosing optimal prior in economic downturn

    OpenAIRE

    Bušs, Ginters

    2009-01-01

    Bayesian inference requires an analyst to set priors. Setting the right prior is crucial for precise forecasts. This paper analyzes how optimal prior changes when an economy is hit by a recession. For this task, an autoregressive distributed lag (ADL) model is chosen. The results show that a sharp economic slowdown changes the optimal prior in two directions. First, it changes the structure of the optimal weight prior, setting smaller weight on the lagged dependent variable compared to varia...

  10. Updating known distribution models for forecasting climate change impact on endangered species.

    Science.gov (United States)

    Muñoz, Antonio-Román; Márquez, Ana Luz; Real, Raimundo

    2013-01-01

    To plan endangered species conservation and to design adequate management programmes, it is necessary to predict their distributional response to climate change, especially under the current situation of rapid change. However, these predictions are customarily done by relating de novo the distribution of the species with climatic conditions with no regard of previously available knowledge about the factors affecting the species distribution. We propose to take advantage of known species distribution models, but proceeding to update them with the variables yielded by climatic models before projecting them to the future. To exemplify our proposal, the availability of suitable habitat across Spain for the endangered Bonelli's Eagle (Aquila fasciata) was modelled by updating a pre-existing model based on current climate and topography to a combination of different general circulation models and Special Report on Emissions Scenarios. Our results suggested that the main threat for this endangered species would not be climate change, since all forecasting models show that its distribution will be maintained and increased in mainland Spain for all the XXI century. We remark on the importance of linking conservation biology with distribution modelling by updating existing models, frequently available for endangered species, considering all the known factors conditioning the species' distribution, instead of building new models that are based on climate change variables only.

  11. Discharge data assimilation in a distributed hydrologic model for flood forecasting purposes

    Science.gov (United States)

    Ercolani, G.; Castelli, F.

    2017-12-01

    Flood early warning systems benefit from accurate river flow forecasts, and data assimilation may improve their reliability. However, the actual enhancement that can be obtained in the operational practice should be investigated in detail and quantified. In this work we assess the benefits that the simultaneous assimilation of discharge observations at multiple locations can bring to flow forecasting through a distributed hydrologic model. The distributed model, MOBIDIC, is part of the operational flood forecasting chain of Tuscany Region in Central Italy. The assimilation system adopts a mixed variational-Monte Carlo approach to update efficiently initial river flow, soil moisture, and a parameter related to runoff production. The evaluation of the system is based on numerous hindcast experiments of real events. The events are characterized by significant rainfall that resulted in both high and relatively low flow in the river network. The area of study is the main basin of Tuscany Region, i.e. Arno river basin, which extends over about 8300 km2 and whose mean annual precipitation is around 800 mm. Arno's mainstream, with its nearly 240 km length, passes through major Tuscan cities, as Florence and Pisa, that are vulnerable to floods (e.g. flood of November 1966). The assimilation tests follow the usage of the model in the forecasting chain, employing the operational resolution in both space and time (500 m and 15 minutes respectively) and releasing new flow forecasts every 6 hours. The assimilation strategy is evaluated in respect to open loop simulations, i.e. runs that do not exploit discharge observations through data assimilation. We compare hydrographs in their entirety, as well as classical performance indexes, as error on peak flow and Nash-Sutcliffe efficiency. The dependence of performances on lead time and location is assessed. Results indicate that the operational forecasting chain can benefit from the developed assimilation system, although with a

  12. Method of forecasting power distribution

    International Nuclear Information System (INIS)

    Kaneto, Kunikazu.

    1981-01-01

    Purpose: To obtain forecasting results at high accuracy by reflecting the signals from neutron detectors disposed in the reactor core on the forecasting results. Method: An on-line computer transfers, to a simulator, those process data such as temperature and flow rate for coolants in each of the sections and various measuring signals such as control rod positions from the nuclear reactor. The simulator calculates the present power distribution before the control operation. The signals from the neutron detectors at each of the positions in the reactor core are estimated from the power distribution and errors are determined based on the estimated values and the measured values to determine the smooth error distribution in the axial direction. Then, input conditions at the time to be forecast are set by a data setter. The simulator calculates the forecast power distribution after the control operation based on the set conditions. The forecast power distribution is corrected using the error distribution. (Yoshino, Y.)

  13. Pell-Sim - dynamic model for forecasting storage and distribution of wood pellets

    International Nuclear Information System (INIS)

    Vinterbaeck, Johan

    2004-01-01

    This study examined the system of wood pellet distribution to residential consumers. The distribution cost for a residential pellet consumer typically represents 30% of the per tonne price and of this share, the inventory cost could be more than 50%. Important administrative activities in physical distribution are forecasting demand and inventory control. One way to improve distribution systems would be to optimise inventory management for pellet distributors. The aim of this study was to propose improvements in pellet distribution management by using tools from systems analysis. The ultimate goal was to present an optimised storage level curve adapted to the mid-Swedish community of Avesta. An internal model for optimising inventory management, Pell-Sim, was constructed, composed of two integrated parts: a simulation unit to forecast residential wood pellet demand and a spreadsheet unit with inventory-related functions. Daily outdoor temperatures basically regulated the simulation unit. An order point system was chosen for reordering. The residential customers of a distribution company were divided into two groups, delivery and collecting customers, which were statistically treated separately. When collecting and delivery customer input inventories were normally distributed in the intervals from 0 to 3500 kg and 6500 kg, respectively, their annual means of total delivery were both about 7000 kg/customer, which was the desired and empirical level. The expected pellet customer orders were negatively correlated to mean daily temperatures, lagging behind about 1 month. Sensitivity analyses showed that monthly results for ordered quantity and total cost were particularly sensitive to ordering and carrying costs. The Pell-Sim programme can easily be adapted for distributors in other geographical regions. (Author)

  14. Pell-Sim - dynamic model for forecasting storage and distribution of wood pellets

    Energy Technology Data Exchange (ETDEWEB)

    Vinterbaeck, Johan [Swedish Univ. of Agricultural Sciences, Dept. of Forest Management and Products, Uppsala (Sweden)

    2004-12-01

    This study examined the system of wood pellet distribution to residential consumers. The distribution cost for a residential pellet consumer typically represents 30% of the per tonne price and of this share, the inventory cost could be more than 50%. Important administrative activities in physical distribution are forecasting demand and inventory control. One way to improve distribution systems would be to optimise inventory management for pellet distributors. The aim of this study was to propose improvements in pellet distribution management by using tools from systems analysis. The ultimate goal was to present an optimised storage level curve adapted to the mid-Swedish community of Avesta. An internal model for optimising inventory management, Pell-Sim, was constructed, composed of two integrated parts: a simulation unit to forecast residential wood pellet demand and a spreadsheet unit with inventory-related functions. Daily outdoor temperatures basically regulated the simulation unit. An order point system was chosen for reordering. The residential customers of a distribution company were divided into two groups, delivery and collecting customers, which were statistically treated separately. When collecting and delivery customer input inventories were normally distributed in the intervals from 0 to 3500 kg and 6500 kg, respectively, their annual means of total delivery were both about 7000 kg/customer, which was the desired and empirical level. The expected pellet customer orders were negatively correlated to mean daily temperatures, lagging behind about 1 month. Sensitivity analyses showed that monthly results for ordered quantity and total cost were particularly sensitive to ordering and carrying costs. The Pell-Sim programme can easily be adapted for distributors in other geographical regions. (Author)

  15. Extending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological model

    Science.gov (United States)

    Li, Ji; Chen, Yangbo; Wang, Huanyu; Qin, Jianming; Li, Jie; Chiao, Sen

    2017-03-01

    Long lead time flood forecasting is very important for large watershed flood mitigation as it provides more time for flood warning and emergency responses. The latest numerical weather forecast model could provide 1-15-day quantitative precipitation forecasting products in grid format, and by coupling this product with a distributed hydrological model could produce long lead time watershed flood forecasting products. This paper studied the feasibility of coupling the Liuxihe model with the Weather Research and Forecasting quantitative precipitation forecast (WRF QPF) for large watershed flood forecasting in southern China. The QPF of WRF products has three lead times, including 24, 48 and 72 h, with the grid resolution being 20 km  × 20 km. The Liuxihe model is set up with freely downloaded terrain property; the model parameters were previously optimized with rain gauge observed precipitation, and re-optimized with the WRF QPF. Results show that the WRF QPF has bias with the rain gauge precipitation, and a post-processing method is proposed to post-process the WRF QPF products, which improves the flood forecasting capability. With model parameter re-optimization, the model's performance improves also. This suggests that the model parameters be optimized with QPF, not the rain gauge precipitation. With the increasing of lead time, the accuracy of the WRF QPF decreases, as does the flood forecasting capability. Flood forecasting products produced by coupling the Liuxihe model with the WRF QPF provide a good reference for large watershed flood warning due to its long lead time and rational results.

  16. Distributional modeling and short-term forecasting of electricity prices by Generalized Additive Models for Location, Scale and Shape

    International Nuclear Information System (INIS)

    Serinaldi, Francesco

    2011-01-01

    In the context of the liberalized and deregulated electricity markets, price forecasting has become increasingly important for energy company's plans and market strategies. Within the class of the time series models that are used to perform price forecasting, the subclasses of methods based on stochastic time series and causal models commonly provide point forecasts, whereas the corresponding uncertainty is quantified by approximate or simulation-based confidence intervals. Aiming to improve the uncertainty assessment, this study introduces the Generalized Additive Models for Location, Scale and Shape (GAMLSS) to model the dynamically varying distribution of prices. The GAMLSS allow fitting a variety of distributions whose parameters change according to covariates via a number of linear and nonlinear relationships. In this way, price periodicities, trends and abrupt changes characterizing both the position parameter (linked to the expected value of prices), and the scale and shape parameters (related to price volatility, skewness, and kurtosis) can be explicitly incorporated in the model setup. Relying on the past behavior of the prices and exogenous variables, the GAMLSS enable the short-term (one-day ahead) forecast of the entire distribution of prices. The approach was tested on two datasets from the widely studied California Power Exchange (CalPX) market, and the less mature Italian Power Exchange (IPEX). CalPX data allow comparing the GAMLSS forecasting performance with published results obtained by different models. The study points out that the GAMLSS framework can be a flexible alternative to several linear and nonlinear stochastic models. - Research Highlights: ► Generalized Additive Models for Location, Scale and Shape (GAMLSS) are used to model electricity prices' time series. ► GAMLSS provide the entire dynamicaly varying distribution function of prices resorting to a suitable set of covariates that drive the instantaneous values of the parameters

  17. Adjusting particle-size distributions to account for aggregation in tephra-deposit model forecasts

    Science.gov (United States)

    Mastin, Larry G.; Van Eaton, Alexa; Durant, A.J.

    2016-01-01

    Volcanic ash transport and dispersion (VATD) models are used to forecast tephra deposition during volcanic eruptions. Model accuracy is limited by the fact that fine-ash aggregates (clumps into clusters), thus altering patterns of deposition. In most models this is accounted for by ad hoc changes to model input, representing fine ash as aggregates with density ρagg, and a log-normal size distribution with median μagg and standard deviation σagg. Optimal values may vary between eruptions. To test the variance, we used the Ash3d tephra model to simulate four deposits: 18 May 1980 Mount St. Helens; 16–17 September 1992 Crater Peak (Mount Spurr); 17 June 1996 Ruapehu; and 23 March 2009 Mount Redoubt. In 192 simulations, we systematically varied μagg and σagg, holding ρagg constant at 600 kg m−3. We evaluated the fit using three indices that compare modeled versus measured (1) mass load at sample locations; (2) mass load versus distance along the dispersal axis; and (3) isomass area. For all deposits, under these inputs, the best-fit value of μagg ranged narrowly between  ∼  2.3 and 2.7φ (0.20–0.15 mm), despite large variations in erupted mass (0.25–50 Tg), plume height (8.5–25 km), mass fraction of fine ( discrete process that is insensitive to eruptive style or magnitude. This result offers the potential for a simple, computationally efficient parameterization scheme for use in operational model forecasts. Further research may indicate whether this narrow range also reflects physical constraints on processes in the evolving cloud.

  18. 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

  19. Neutron flux distribution forecasting device of reactor

    International Nuclear Information System (INIS)

    Uematsu, Hitoshi

    1991-01-01

    A neutron flux distribution is forecast by using current data obtained from a reactor. That is, the device of the present invention comprises (1) a neutron flux monitor disposed in various positions in the reactor, (2) a forecasting means for calculating and forecasting a one-dimensional neutron flux distribution relative to imaginable events by using data obtained from the neutron flux monitor and physical models, and (3) a display means for displaying the results forecast in the forecasting means to a reactor operation console. Since the forecast values for the one-dimensional neutron flux distribution relative to the imaginable events are calculated in the device of the present invention by using data obtained from the neutron flux monitor and the physical models, the data as a base of the calculation are new and the period for calculating the forecast values can be shortened. Accordingly, although there is a worry of providing some errors in the forecast values, they can be utilized sufficiently as reference data. As a result, the reactor can be operated more appropriately. (I.N.)

  20. Power distribution forecasting device for reactors

    International Nuclear Information System (INIS)

    Tsukii, Makoto

    1981-01-01

    Purpose: To save expensive calculations on the forecasting of reactor power distribution. Constitution: Core status (CSD) such as entire coolant flow rate, pressures in the reactor, temperatures at the outlet and inlet and positions for control rods are inputted into a power distribution calculation device to calculate the power distribution based on physical models intermittently. Further, present power distribution is calculated based on in-core neutron flux measured values and CSD in a process control computer. Further, the ratio of the calculation results of the latter to those of the former is calculated, stored and inputted into a correction device to correct the forecast power distribution obtained by the power distribution calculation device. This enables to forecast the power distribution with excellent responsivity in the reactor site. (Furukawa, Y.)

  1. Ensemble forecasting of species distributions.

    Science.gov (United States)

    Araújo, Miguel B; New, Mark

    2007-01-01

    Concern over implications of climate change for biodiversity has led to the use of bioclimatic models to forecast the range shifts of species under future climate-change scenarios. Recent studies have demonstrated that projections by alternative models can be so variable as to compromise their usefulness for guiding policy decisions. Here, we advocate the use of multiple models within an ensemble forecasting framework and describe alternative approaches to the analysis of bioclimatic ensembles, including bounding box, consensus and probabilistic techniques. We argue that, although improved accuracy can be delivered through the traditional tasks of trying to build better models with improved data, more robust forecasts can also be achieved if ensemble forecasts are produced and analysed appropriately.

  2. FORECASTING MODELS IN MANAGEMENT

    OpenAIRE

    Sindelar, Jiri

    2008-01-01

    This article deals with the problems of forecasting models. First part of the article is dedicated to definition of the relevant areas (vertical and horizontal pillar of definition) and then the forecasting model itself is defined; as article presents theoretical background for further primary research, this definition is crucial. Finally the position of forecasting models within the management system is identified. The paper is a part of the outputs of FEM CULS grant no. 1312/11/3121.

  3. Forecasting with Dynamic Regression Models

    CERN Document Server

    Pankratz, Alan

    2012-01-01

    One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.

  4. Evaluating the impact of improvements to the FLAMBE smoke source model on forecasts of aerosol distribution from NAAPS

    Science.gov (United States)

    Hyer, E. J.; Reid, J. S.

    2006-12-01

    As more forecast models aim to include aerosol and chemical species, there is a need for source functions for biomass burning emissions that are accurate, robust, and operable in real-time. NAAPS is a global aerosol forecast model running every six hours and forecasting distributions of biomass burning, industrial sulfate, dust, and sea salt aerosols. This model is run operationally by the U.S. Navy as an aid to planning. The smoke emissions used as input to the model are calculated from the data collected by the FLAMBE system, driven by near-real-time active fire data from GOES WF_ABBA and MODIS Rapid Response. The smoke source function uses land cover data to predict properties of detected fires based on literature data from experimental burns. This scheme is very sensitive to the choice of land cover data sets. In areas of rapid land cover change, the use of static land cover data can produce artifactual changes in emissions unrelated to real changes in fire patterns. In South America, this change may be as large as 40% over five years. We demonstrate the impact of a modified land cover scheme on FLAMBE emissions and NAAPS forecasts, including a fire size algorithm developed using MODIS burned area data. We also describe the effects of corrections to emissions estimates for cloud and satellite coverage. We outline areas where existing data sources are incomplete and improvements are required to achieve accurate modeling of biomass burning emissions in real time.

  5. 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...

  6. The Effect of Model Grid Resolution on the Distributed Hydrologic Simulations for Forecasting Stream Flows and Reservoir Storage

    Science.gov (United States)

    Turnbull, S. J.

    2017-12-01

    Within the US Army Corps of Engineers (USACE), reservoirs are typically operated according to a rule curve that specifies target water levels based on the time of year. The rule curve is intended to maximize flood protection by specifying releases of water before the dominant rainfall period for a region. While some operating allowances are permissible, generally the rule curve elevations must be maintained. While this operational approach provides for the required flood control purpose, it may not result in optimal reservoir operations for multi-use impoundments. In the Russian River Valley of California a multi-agency research effort called Forecast-Informed Reservoir Operations (FIRO) is assessing the application of forecast weather and streamflow predictions to potentially enhance the operation of reservoirs in the watershed. The focus of the study has been on Lake Mendocino, a USACE project important for flood control, water supply, power generation and ecological flows. As part of this effort the Engineer Research and Development Center is assessing the ability of utilizing the physics based, distributed watershed model Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model to simulate stream flows, reservoir stages, and discharges while being driven by weather forecast products. A key question in this application is the effect of watershed model resolution on forecasted stream flows. To help resolve this question, GSSHA models of multiple grid resolutions, 30, 50, and 270m, were developed for the upper Russian River, which includes Lake Mendocino. The models were derived from common inputs: DEM, soils, land use, stream network, reservoir characteristics, and specified inflows and discharges. All the models were calibrated in both event and continuous simulation mode using measured precipitation gages and then driven with the West-WRF atmospheric model in prediction mode to assess the ability of the model to function in short term, less than one week

  7. The distribution of wind power forecast errors from operational systems

    Energy Technology Data Exchange (ETDEWEB)

    Hodge, Bri-Mathias; Ela, Erik; Milligan, Michael

    2011-07-01

    Wind power forecasting is one important tool in the integration of large amounts of renewable generation into the electricity system. Wind power forecasts from operational systems are not perfect, and thus, an understanding of the forecast error distributions can be important in system operations. In this work, we examine the errors from operational wind power forecasting systems, both for a single wind plant and for an entire interconnection. The resulting error distributions are compared with the normal distribution and the distribution obtained from the persistence forecasting model at multiple timescales. A model distribution is fit to the operational system forecast errors and the potential impact on system operations highlighted through the generation of forecast confidence intervals. (orig.)

  8. Sensitivity and Interaction Analysis Based on Sobol’ Method and Its Application in a Distributed Flood Forecasting Model

    Directory of Open Access Journals (Sweden)

    Hui Wan

    2015-06-01

    Full Text Available Sensitivity analysis is a fundamental approach to identify the most significant and sensitive parameters, helping us to understand complex hydrological models, particularly for time-consuming distributed flood forecasting models based on complicated theory with numerous parameters. Based on Sobol’ method, this study compared the sensitivity and interactions of distributed flood forecasting model parameters with and without accounting for correlation. Four objective functions: (1 Nash–Sutcliffe efficiency (ENS; (2 water balance coefficient (WB; (3 peak discharge efficiency (EP; and (4 time to peak efficiency (ETP were implemented to the Liuxihe model with hourly rainfall-runoff data collected in the Nanhua Creek catchment, Pearl River, China. Contrastive results for the sensitivity and interaction analysis were also illustrated among small, medium, and large flood magnitudes. Results demonstrated that the choice of objective functions had no effect on the sensitivity classification, while it had great influence on the sensitivity ranking for both uncorrelated and correlated cases. The Liuxihe model behaved and responded uniquely to various flood conditions. The results also indicated that the pairwise parameters interactions revealed a non-ignorable contribution to the model output variance. Parameters with high first or total order sensitivity indices presented a corresponding high second order sensitivity indices and correlation coefficients with other parameters. Without considering parameter correlations, the variance contributions of highly sensitive parameters might be underestimated and those of normally sensitive parameters might be overestimated. This research laid a basic foundation to improve the understanding of complex model behavior.

  9. Probabilistic model to forecast earthquakes in the Zemmouri (Algeria) seismoactive area on the basis of moment magnitude scale distribution functions

    Science.gov (United States)

    Baddari, Kamel; Makdeche, Said; Bellalem, Fouzi

    2013-02-01

    Based on the moment magnitude scale, a probabilistic model was developed to predict the occurrences of strong earthquakes in the seismoactive area of Zemmouri, Algeria. Firstly, the distributions of earthquake magnitudes M i were described using the distribution function F 0(m), which adjusts the magnitudes considered as independent random variables. Secondly, the obtained result, i.e., the distribution function F 0(m) of the variables M i was used to deduce the distribution functions G(x) and H(y) of the variables Y i = Log M 0,i and Z i = M 0,i , where (Y i)i and (Z i)i are independent. Thirdly, some forecast for moments of the future earthquakes in the studied area is given.

  10. Forecasting the regional distribution and sufficiency of physicians in Japan with a coupled system dynamics-geographic information system model.

    Science.gov (United States)

    Ishikawa, Tomoki; Fujiwara, Kensuke; Ohba, Hisateru; Suzuki, Teppei; Ogasawara, Katsuhiko

    2017-09-12

    In Japan, the shortage of physicians has been recognized as a major medical issue. In our previous study, we reported that the absolute shortage will be resolved in the long term, but maldistribution among specialties will persist. To address regional shortage, several Japanese medical schools increased existing quota and established "regional quotas." This study aims to assist policy makers in designing effective policies; we built a model for forecasting physician numbers by region to evaluate future physician supply-demand balances. For our case study, we selected Hokkaido Prefecture in Japan, a region displaying disparities in healthcare services availability between urban and rural areas. We combined a system dynamics (SD) model with geographic information system (GIS) technology to analyze the dynamic change in spatial distribution of indicators. For Hokkaido overall and for each secondary medical service area (SMSA) within the prefecture, we analyzed the total number of practicing physicians. For evaluating absolute shortage and maldistribution, we calculated sufficiency levels and Gini coefficient. Our study covered the period 2010-2030 in 5-year increments. According to our forecast, physician shortage in Hokkaido Prefecture will largely be resolved by 2020. Based on current policies, we forecast that four SMSAs in Hokkaido will continue to experience physician shortages past that date, but only one SMSA would still be understaffed in 2030. The results show the possibility that diminishing imbalances between SMSAs would not necessarily mean that regional maldistribution would be eliminated, as seen from the sufficiency levels of the various SMSAs. Urgent steps should be taken to place doctors in areas where our forecasting model predicts that physician shortages could occur in the future.

  11. Development of distributed topographical forecasting model for wind resource assessment using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Narayana, P.B. [Green Life Energy Solutions LLP, Secunderabad (India); Rao, S.S. [National Institute of Technology. Dept. of Mechanical Engineering, Warangal (India); Reddy, K.H. [JNT Univ.. Dept. of Mechanical Engineering, Anantapur (India)

    2012-07-01

    Economics of wind power projects largely depend on the availability of wind power density. Wind resource assessment is a study estimating wind speeds and wind power densities in the region under consideration. The accuracy and reliability of data sets comprising of wind speeds and wind power densities at different heights per topographic region characterized by elevation or mean sea level, is important for wind power projects. Indian Wind Resource Assessment program conducted in 80's consisted of wind data measured by monitoring stations at different topographies in order to measure wind power density values at 25 and 50 meters above the ground level. In this paper, an attempt has been made to assess wind resource at a given location using artificial neural networks. Existing wind resource data has been used to train the neural networks. Location topography (characterized by longitude, latitude and mean sea level), air density, mean annual wind speed (MAWS) are used as inputs to the neural network. Mean annual wind power density (MAWPD) in watt/m{sup 2} is predicted for a new topographic location. Simple back propagation based neural network has been found to be sufficient for predicting these values with suitable accuracy. This model is closely linked to the problem of wind energy forecasting considering the variations of specific atmospheric variables with time horizons. This model will help the wind farm developers to have an initial estimation of the wind energy potential at a particular topography. (Author)

  12. System for forecasting a reactor power distribution

    International Nuclear Information System (INIS)

    Motoda, Hiroshi; Nishizawa, Yasuo.

    1976-01-01

    Purpose: To dispense with frequent running of detector in a BWR type reactor and permit calculation of the prevailing value and forecast value of power distribution in a specified region in an on-line basis. Constitution: The prevailing power distribution P sub(OZ) (where Z indicates a position in the axial direction) at a given position is estimated by prevailing power distribution estimating means, and the average prevailing power distribution Q sub(OZ) in the core is estimated while making correction of a primary neutron distribution model by core average characteristic measuring means. Then, the estimated core average power distribution Q sub(Z) after alteration of the core flow rate or alteration of Xe concentration is estimated by core average power distribution estimating means. At this time, a forecast power distribution P sub(Z) in a specified region after alteration of the flow rate or alteration of the Xe concentration is calculated on the basis of a relation P sub(Z) = (Q sub(Z)/Q sub(OZ)) by using P sub(OZ), Q sub(OZ) and Q sub(Z). The above calculations are carried out in a short period of time by using a process computer. (Ikeda, J.)

  13. A Forecast Model for Unemployment by Education

    DEFF Research Database (Denmark)

    Tranæs, Torben; Larsen, Anders Holm; Groes, Niels

    1994-01-01

    We present a dynamic forecast model for the labour market: demand for labour by education and the distribution of labour by education among industries are determined endogenously with overall demand by industry given exogenously. The model is derived from a simple behavioural equation based on a ...... for educational groups, where the initial forecast year is a change point for unemployment....

  14. IFIS Model-Plus: A Web-Based GUI for Visualization, Comparison and Evaluation of Distributed Flood Forecasts and Hindcasts

    Science.gov (United States)

    Krajewski, W. F.; Della Libera Zanchetta, A.; Mantilla, R.; Demir, I.

    2017-12-01

    This work explores the use of hydroinformatics tools to provide an user friendly and accessible interface for executing and assessing the output of realtime flood forecasts using distributed hydrological models. The main result is the implementation of a web system that uses an Iowa Flood Information System (IFIS)-based environment for graphical displays of rainfall-runoff simulation results for both real-time and past storm events. It communicates with ASYNCH ODE solver to perform large-scale distributed hydrological modeling based on segmentation of the terrain into hillslope-link hydrologic units. The cyber-platform also allows hindcast of model performance by testing multiple model configurations and assumptions of vertical flows in the soils. The scope of the currently implemented system is the entire set of contributing watersheds for the territory of the state of Iowa. The interface provides resources for visualization of animated maps for different water-related modeled states of the environment, including flood-waves propagation with classification of flood magnitude, runoff generation, surface soil moisture and total water column in the soil. Additional tools for comparing different model configurations and performing model evaluation by comparing to observed variables at monitored sites are also available. The user friendly interface has been published to the web under the URL http://ifis.iowafloodcenter.org/ifis/sc/modelplus/.

  15. Coupling impervious surface rate derived from satellite remote sensing with distributed hydrological model for highly urbanized watershed flood forecasting

    Science.gov (United States)

    Dong, L.

    2017-12-01

    Abstract: The original urban surface structure changed a lot because of the rapid development of urbanization. Impermeable area has increased a lot. It causes great pressure for city flood control and drainage. Songmushan reservoir basin with high degree of urbanization is taken for an example. Pixel from Landsat is decomposed by Linear spectral mixture model and the proportion of urban area in it is considered as impervious rate. Based on impervious rate data before and after urbanization, an physically based distributed hydrological model, Liuxihe Model, is used to simulate the process of hydrology. The research shows that the performance of the flood forecasting of high urbanization area carried out with Liuxihe Model is perfect and can meet the requirement of the accuracy of city flood control and drainage. The increase of impervious area causes conflux speed more quickly and peak flow to be increased. It also makes the time of peak flow advance and the runoff coefficient increase. Key words: Liuxihe Model; Impervious rate; City flood control and drainage; Urbanization; Songmushan reservoir basin

  16. Regional hydrological models for distributed flash-floods forecasting: towards an estimation of potential impacts and damages

    Science.gov (United States)

    Le Bihan, Guillaume; Payrastre, Olivier; Gaume, Eric; Pons, Frederic; Moncoulon, David

    2016-04-01

    Hydrometeorological forecasting is an essential component of real-time flood management. The information it provides is of great help for crisis managers to anticipate the inundations and the associated risks. In the particular case of flash-floods, which may affect a large amount of small watersheds spread over the territory (up to 300 000 km of waterways considering a drained area of 5 km² minimum in France), appropriate flood forecasting systems are still under development. In France, highly distributed hydrological models have been implemented, enabling a real-time assessment of the potential intensity of flash-floods from the records of weather radars: AIGA-hydro system (Lavabre et al., 2005; Javelle et al., 2014), PreDiFlood project (Naulin et al., 2013). The approach presented here aims to go one step further by offering a direct assessment of the potential impacts of the simulated floods on inhabited areas. This approach is based on an a priori analysis of the study area in order (1) to evaluate with a simplified hydraulic approach (DTM treatment) the potentially flooded areas for different discharge levels, and (2) to identify the associated buildings and/or population at risk from geographic databases. This preliminary analysis enables to build an impact model (discharge-impact curve) on each river reach, which is then used to directly estimate the potentially affected assets based on a distributed rainfall runoff model. The overall principle of this approach was already presented at the 8th Hymex workshop. Therefore, the presentation will be here focused on the first validation results in terms of (1) accuracy of flooded areas simulated from DTM treatments, and (2) relevance of estimated impacts. The inundated areas simulated were compared to the European Directive cartography results (where available), showing an overall good correspondence in a large majority of cases, but also very significant errors for approximatively 10% of the river reaches

  17. Flow Forecasting using Deterministic Updating of Water Levels in Distributed Hydrodynamic Urban Drainage Models

    DEFF Research Database (Denmark)

    Hansen, Lisbet Sneftrup; Borup, Morten; Moller, Arne

    2014-01-01

    drainage models and reduce a number of unavoidable discrepancies between the model and reality. The latter can be achieved partly by inserting measured water levels from the sewer system into the model. This article describes how deterministic updating of model states in this manner affects a simulation...

  18. Modeling Soak-Time Distribution of Trips for Mobile Source Emissions Forecasting: Techniques and Applications

    Science.gov (United States)

    2000-08-01

    The soak-time of vehicle trip starts is defined as the duration of time in which the vehicle's engine is not operating and that precedes a successful vehicle start. The temporal distribution of the soak-time in an area is an important determinant of ...

  19. State updating of a distributed hydrological model with Ensemble Kalman Filtering: effects of updating frequency and observation network density on forecast accuracy

    Directory of Open Access Journals (Sweden)

    O. Rakovec

    2012-09-01

    Full Text Available This paper presents a study on the optimal setup for discharge assimilation within a spatially distributed hydrological model. The Ensemble Kalman filter (EnKF is employed to update the grid-based distributed states of such an hourly spatially distributed version of the HBV-96 model. By using a physically based model for the routing, the time delay and attenuation are modelled more realistically. The discharge and states at a given time step are assumed to be dependent on the previous time step only (Markov property.

    Synthetic and real world experiments are carried out for the Upper Ourthe (1600 km2, a relatively quickly responding catchment in the Belgian Ardennes. We assess the impact on the forecasted discharge of (1 various sets of the spatially distributed discharge gauges and (2 the filtering frequency. The results show that the hydrological forecast at the catchment outlet is improved by assimilating interior gauges. This augmentation of the observation vector improves the forecast more than increasing the updating frequency. In terms of the model states, the EnKF procedure is found to mainly change the pdfs of the two routing model storages, even when the uncertainty in the discharge simulations is smaller than the defined observation uncertainty.

  20. Fog prediction using the modified asymptotic liquid water content vertical distribution formulation with the Weather Research and Forecasting model

    Science.gov (United States)

    Kim, E.; Lee, S.; Kim, J.; Chae, D.

    2017-12-01

    Fog forecasts have difficulty in forecasting due to temporal and spatial resolution problems, high numerical computations, complicated mechanisms related to turbulence in order to analyze the fog in the model, and a lack of appropriate fog physical processes. Conventional fog prediction is based on the surface visibility threshold "fog diagnosis method is based on the fog related variables near the surface, such as visibility, low stratus, relative humidity and wind speed but this method only predicts fog occurrence not fog intensity. To improve this, a new fog diagnostic scheme, based on an asymptotic analytical study of radiation fog (Zhou and Ferrier 2008, ZF08) is to increase the accuracy of fog prediction by calculating the vertical LWC considering cooling, turbulence and droplet settling, visibility, surface relative humidity and low stratus. In this study, we intend to improve fog prediction through the Weather Research and Forecasting (WRF) model using high-resolution data. Although the prediction accuracy can be improved by combining the WRF Planetary Boundary Layer (PBL) scheme and 1 dimension (1D) model, it is necessary to increase the vertical resolution in the boundary layer to implement the fog formation and persistence mechanism in the internal boundary layer in the PBL more accurately, we'll modify the algorithm to enhance the effects of turbulence and then compare the newly predicted fog and observations to determine the accuracy of the forecast of the fog occurring on the Korean peninsula.

  1. Uncertainty Analysis of Multi-Model Flood Forecasts

    Directory of Open Access Journals (Sweden)

    Erich J. Plate

    2015-12-01

    Full Text Available This paper demonstrates, by means of a systematic uncertainty analysis, that the use of outputs from more than one model can significantly improve conditional forecasts of discharges or water stages, provided the models are structurally different. Discharge forecasts from two models and the actual forecasted discharge are assumed to form a three-dimensional joint probability density distribution (jpdf, calibrated on long time series of data. The jpdf is decomposed into conditional probability density distributions (cpdf by means of Bayes formula, as suggested and explored by Krzysztofowicz in a series of papers. In this paper his approach is simplified to optimize conditional forecasts for any set of two forecast models. Its application is demonstrated by means of models developed in a study of flood forecasting for station Stung Treng on the middle reach of the Mekong River in South-East Asia. Four different forecast models were used and pairwise combined: forecast with no model, with persistence model, with a regression model, and with a rainfall-runoff model. Working with cpdfs requires determination of dependency among variables, for which linear regressions are required, as was done by Krzysztofowicz. His Bayesian approach based on transforming observed probability distributions of discharges and forecasts into normal distributions is also explored. Results obtained with his method for normal prior and likelihood distributions are identical to results from direct multiple regressions. Furthermore, it is shown that in the present case forecast accuracy is only marginally improved, if Weibull distributed basic data were converted into normally distributed variables.

  2. Designing the input vector to ANN-based models for short-term load forecast in electricity distribution systems

    International Nuclear Information System (INIS)

    Santos, P.J.; Martins, A.G.; Pires, A.J.

    2007-01-01

    The present trend to electricity market restructuring increases the need for reliable short-term load forecast (STLF) algorithms, in order to assist electric utilities in activities such as planning, operating and controlling electric energy systems. Methodologies such as artificial neural networks (ANN) have been widely used in the next hour load forecast horizon with satisfactory results. However, this type of approach has had some shortcomings. Usually, the input vector (IV) is defined in a arbitrary way, mainly based on experience, on engineering judgment criteria and on concern about the ANN dimension, always taking into consideration the apparent correlations within the available endogenous and exogenous data. In this paper, a proposal is made of an approach to define the IV composition, with the main focus on reducing the influence of trial-and-error and common sense judgments, which usually are not based on sufficient evidence of comparative advantages over previous alternatives. The proposal includes the assessment of the strictly necessary instances of the endogenous variable, both from the point of view of the contiguous values prior to the forecast to be made, and of the past values representing the trend of consumption at homologous time intervals of the past. It also assesses the influence of exogenous variables, again limiting their presence at the IV to the indispensable minimum. A comparison is made with two alternative IV structures previously proposed in the literature, also applied to the distribution sector. The paper is supported by a real case study at the distribution sector. (author)

  3. NYHOPS Forecast Model Results

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — 3D Marine Nowcast/Forecast System for the New York Bight NYHOPS subdomain. Currents, waves, surface meteorology, and water conditions.

  4. Operational flood-forecasting in the Piemonte region – development and verification of a fully distributed physically-oriented hydrological model

    Directory of Open Access Journals (Sweden)

    D. Rabuffetti

    2009-03-01

    Full Text Available A hydrological model for real time flood forecasting to Civil Protection services requires reliability and rapidity. At present, computational capabilities overcome the rapidity needs even when a fully distributed hydrological model is adopted for a large river catchment as the Upper Po river basin closed at Ponte Becca (nearly 40 000 km2. This approach allows simulating the whole domain and obtaining the responses of large as well as of medium and little sized sub-catchments. The FEST-WB hydrological model (Mancini, 1990; Montaldo et al., 2007; Rabuffetti et al., 2008 is implemented. The calibration and verification activities are based on more than 100 flood events, occurred along the main tributaries of the Po river in the period 2000–2003. More than 300 meteorological stations are used to obtain the forcing fields, 10 cross sections with continuous and reliable discharge time series are used for calibration while verification is performed on about 40 monitored cross sections. Furthermore meteorological forecasting models are used to force the hydrological model with Quantitative Precipitation Forecasts (QPFs for 36 h horizon in "operational setting" experiments. Particular care is devoted to understanding how QPF affects the accuracy of the Quantitative Discharge Forecasts (QDFs and to assessing the QDF uncertainty impact on the warning system reliability. Results are presented either in terms of QDF and of warning issues highlighting the importance of an "operational based" verification approach.

  5. Technical note: Combining quantile forecasts and predictive distributions of streamflows

    Science.gov (United States)

    Bogner, Konrad; Liechti, Katharina; Zappa, Massimiliano

    2017-11-01

    The enhanced availability of many different hydro-meteorological modelling and forecasting systems raises the issue of how to optimally combine this great deal of information. Especially the usage of deterministic and probabilistic forecasts with sometimes widely divergent predicted future streamflow values makes it even more complicated for decision makers to sift out the relevant information. In this study multiple streamflow forecast information will be aggregated based on several different predictive distributions, and quantile forecasts. For this combination the Bayesian model averaging (BMA) approach, the non-homogeneous Gaussian regression (NGR), also known as the ensemble model output statistic (EMOS) techniques, and a novel method called Beta-transformed linear pooling (BLP) will be applied. By the help of the quantile score (QS) and the continuous ranked probability score (CRPS), the combination results for the Sihl River in Switzerland with about 5 years of forecast data will be compared and the differences between the raw and optimally combined forecasts will be highlighted. The results demonstrate the importance of applying proper forecast combination methods for decision makers in the field of flood and water resource management.

  6. Flood forecasting using a fully distributed model: application of the TOPKAPI model to the Upper Xixian Catchment

    Directory of Open Access Journals (Sweden)

    Z. Liu

    2005-01-01

    Full Text Available TOPKAPI is a physically-based, fully distributed hydrological model with a simple and parsimonious parameterisation. The original TOPKAPI is structured around five modules that represent evapotranspiration, snowmelt, soil water, surface water and channel water, respectively. Percolation to deep soil layers was ignored in the old version of the TOPKAPI model since it was not important in the basins to which the model was originally applied. Based on published literature, this study developed a new version of the TOPKAPI model, in which the new modules of interception, infiltration, percolation, groundwater flow and lake/reservoir routing are included. This paper presents an application study that makes a first attempt to derive information from public domains through the internet on the topography, soil and land use types for a case study Chinese catchment - the Upper Xixian catchment in Huaihe River with an area of about 10000 km2, and apply a new version of TOPKAPI to the catchment for flood simulation. A model parameter value adjustment was performed using six months of the 1998 dataset. Calibration did not use a curve fitting process, but was chiefly based upon moderate variations of parameter values from those estimated on physical grounds, as is common in traditional calibration. The hydrometeorological dataset of 2002 was then used to validate the model, both against the outlet discharge as well as at an internal gauging station. Finally, to complete the model performance analysis, parameter uncertainty and its effects on predictive uncertainty were also assessed by estimating a posterior parameter probability density via Bayesian inference.

  7. State updating of a distributed hydrological model with Ensemble Kalman Filtering: Effects of updating frequency and observation network density on forecast accuracy

    Science.gov (United States)

    Rakovec, O.; Weerts, A.; Hazenberg, P.; Torfs, P.; Uijlenhoet, R.

    2012-12-01

    This paper presents a study on the optimal setup for discharge assimilation within a spatially distributed hydrological model (Rakovec et al., 2012a). The Ensemble Kalman filter (EnKF) is employed to update the grid-based distributed states of such an hourly spatially distributed version of the HBV-96 model. By using a physically based model for the routing, the time delay and attenuation are modelled more realistically. The discharge and states at a given time step are assumed to be dependent on the previous time step only (Markov property). Synthetic and real world experiments are carried out for the Upper Ourthe (1600 km2), a relatively quickly responding catchment in the Belgian Ardennes. The uncertain precipitation model forcings were obtained using a time-dependent multivariate spatial conditional simulation method (Rakovec et al., 2012b), which is further made conditional on preceding simulations. We assess the impact on the forecasted discharge of (1) various sets of the spatially distributed discharge gauges and (2) the filtering frequency. The results show that the hydrological forecast at the catchment outlet is improved by assimilating interior gauges. This augmentation of the observation vector improves the forecast more than increasing the updating frequency. In terms of the model states, the EnKF procedure is found to mainly change the pdfs of the two routing model storages, even when the uncertainty in the discharge simulations is smaller than the defined observation uncertainty. Rakovec, O., Weerts, A. H., Hazenberg, P., Torfs, P. J. J. F., and Uijlenhoet, R.: State updating of a distributed hydrological model with Ensemble Kalman Filtering: effects of updating frequency and observation network density on forecast accuracy, Hydrol. Earth Syst. Sci. Discuss., 9, 3961-3999, doi:10.5194/hessd-9-3961-2012, 2012a. Rakovec, O., Hazenberg, P., Torfs, P. J. J. F., Weerts, A. H., and Uijlenhoet, R.: Generating spatial precipitation ensembles: impact of

  8. Assimilation of ground and satellite snow observations in a distributed hydrologic model to improve water supply forecasts in the Upper Colorado River Basin

    Science.gov (United States)

    Micheletty, P. D.; Day, G. N.; Quebbeman, J.; Carney, S.; Park, G. H.

    2016-12-01

    The Upper Colorado River Basin above Lake Powell is a major source of water supply for 25 million people and provides irrigation water for 3.5 million acres. Approximately 85% of the annual runoff is produced from snowmelt. Water supply forecasts of the April-July runoff produced by the National Weather Service (NWS) Colorado Basin River Forecast Center (CBRFC), are critical to basin water management. This project leverages advanced distributed models, datasets, and snow data assimilation techniques to improve operational water supply forecasts made by CBRFC in the Upper Colorado River Basin. The current work will specifically focus on improving water supply forecasts through the implementation of a snow data assimilation process coupled with the Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM). Three types of observations will be used in the snow data assimilation system: satellite Snow Covered Area (MODSCAG), satellite Dust Radiative Forcing in Snow (MODDRFS), and SNOTEL Snow Water Equivalent (SWE). SNOTEL SWE provides the main source of high elevation snowpack information during the snow season, however, these point measurement sites are carefully selected to provide consistent indices of snowpack, and may not be representative of the surrounding watershed. We address this problem by transforming the SWE observations to standardized deviates and interpolating the standardized deviates using a spatial regression model. The interpolation process will also take advantage of the MODIS Snow Covered Area and Grainsize (MODSCAG) product to inform the model on the spatial distribution of snow. The interpolated standardized deviates are back-transformed and used in an Ensemble Kalman Filter (EnKF) to update the model simulated SWE. The MODIS Dust Radiative Forcing in Snow (MODDRFS) product will be used more directly through temporary adjustments to model snowmelt parameters, which should improve melt estimates in areas affected by dust on snow. In

  9. Relating Tropical Cyclone Track Forecast Error Distributions with Measurements of Forecast Uncertainty

    Science.gov (United States)

    2016-03-01

    CYCLONE TRACK FORECAST ERROR DISTRIBUTIONS WITH MEASUREMENTS OF FORECAST UNCERTAINTY by Nicholas M. Chisler March 2016 Thesis Advisor...March 2016 3. REPORT TYPE AND DATES COVERED Master’s thesis 4. TITLE AND SUBTITLE RELATING TROPICAL CYCLONE TRACK FORECAST ERROR DISTRIBUTIONS...WITH MEASUREMENTS OF FORECAST UNCERTAINTY 5. FUNDING NUMBERS 6. AUTHOR(S) Nicholas M. Chisler 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES

  10. Load forecasting method considering temperature effect for distribution network

    Directory of Open Access Journals (Sweden)

    Meng Xiao Fang

    2016-01-01

    Full Text Available To improve the accuracy of load forecasting, the temperature factor was introduced into the load forecasting in this paper. This paper analyzed the characteristics of power load variation, and researched the rule of the load with the temperature change. Based on the linear regression analysis, the mathematical model of load forecasting was presented with considering the temperature effect, and the steps of load forecasting were given. Used MATLAB, the temperature regression coefficient was calculated. Using the load forecasting model, the full-day load forecasting and time-sharing load forecasting were carried out. By comparing and analyzing the forecast error, the results showed that the error of time-sharing load forecasting method was small in this paper. The forecasting method is an effective method to improve the accuracy of load forecasting.

  11. Evaluation of statistical models for forecast errors from the HBV model

    Science.gov (United States)

    Engeland, Kolbjørn; Renard, Benjamin; Steinsland, Ingelin; Kolberg, Sjur

    2010-04-01

    SummaryThree statistical models for the forecast errors for inflow into the Langvatn reservoir in Northern Norway have been constructed and tested according to the agreement between (i) the forecast distribution and the observations and (ii) median values of the forecast distribution and the observations. For the first model observed and forecasted inflows were transformed by the Box-Cox transformation before a first order auto-regressive model was constructed for the forecast errors. The parameters were conditioned on weather classes. In the second model the Normal Quantile Transformation (NQT) was applied on observed and forecasted inflows before a similar first order auto-regressive model was constructed for the forecast errors. For the third model positive and negative errors were modeled separately. The errors were first NQT-transformed before conditioning the mean error values on climate, forecasted inflow and yesterday's error. To test the three models we applied three criterions: we wanted (a) the forecast distribution to be reliable; (b) the forecast intervals to be narrow; (c) the median values of the forecast distribution to be close to the observed values. Models 1 and 2 gave almost identical results. The median values improved the forecast with Nash-Sutcliffe R eff increasing from 0.77 for the original forecast to 0.87 for the corrected forecasts. Models 1 and 2 over-estimated the forecast intervals but gave the narrowest intervals. Their main drawback was that the distributions are less reliable than Model 3. For Model 3 the median values did not fit well since the auto-correlation was not accounted for. Since Model 3 did not benefit from the potential variance reduction that lies in bias estimation and removal it gave on average wider forecasts intervals than the two other models. At the same time Model 3 on average slightly under-estimated the forecast intervals, probably explained by the use of average measures to evaluate the fit.

  12. Forecasting Value-at-Risk under Different Distributional Assumptions

    Directory of Open Access Journals (Sweden)

    Manuela Braione

    2016-01-01

    Full Text Available Financial asset returns are known to be conditionally heteroskedastic and generally non-normally distributed, fat-tailed and often skewed. These features must be taken into account to produce accurate forecasts of Value-at-Risk (VaR. We provide a comprehensive look at the problem by considering the impact that different distributional assumptions have on the accuracy of both univariate and multivariate GARCH models in out-of-sample VaR prediction. The set of analyzed distributions comprises the normal, Student, Multivariate Exponential Power and their corresponding skewed counterparts. The accuracy of the VaR forecasts is assessed by implementing standard statistical backtesting procedures used to rank the different specifications. The results show the importance of allowing for heavy-tails and skewness in the distributional assumption with the skew-Student outperforming the others across all tests and confidence levels.

  13. Global-warming forecasting models

    International Nuclear Information System (INIS)

    Moeller, K.P.

    1992-01-01

    In spite of an annual man-made quantity of about 20 billion tons, carbon dioxide has remained a trace gas in the atmosphere (350 ppm at present). The reliability of model calculations which forecast temperatures is dicussed in view of the world-wide increase in carbon dioxides. Computer simulations reveal a general, serious threat to the future of mankind. (DG) [de

  14. Distribution load forecast with interactive correction of horizon loads

    International Nuclear Information System (INIS)

    Glamochanin, V.; Andonov, D.; Gagovski, I.

    1994-01-01

    This paper presents the interactive distribution load forecast application that performs the distribution load forecast with interactive correction of horizon loads. It consists of two major parts implemented in Fortran and Visual Basic. The Fortran part is used for the forecasts computations. It consists of two methods: Load Transfer Coupling Curve Fitting (LTCCF) and load Forecast Using Curve Shape Clustering (FUCSC). LTCCF is used to 'correct' the contaminated data because of load transfer among neighboring distribution areas. FUCSC uses curve shape clustering to forecast the distribution loads of small areas. The forecast for each small area is achieved by using the shape of corresponding cluster curve. The comparison of forecasted loads of the area with historical data will be used as a tool for the correction of the estimated horizon load. The Visual Basic part is used to provide flexible interactive user-friendly environment. (author). 5 refs., 3 figs

  15. Forecasting distributions of large federal-lands fires utilizing satellite and gridded weather information

    Science.gov (United States)

    H.K. Preisler; R.E. Burgan; J.C. Eidenshink; J.M. Klaver; R.W. Klaver

    2009-01-01

    The current study presents a statistical model for assessing the skill of fire danger indices and for forecasting the distribution of the expected numbers of large fires over a given region and for the upcoming week. The procedure permits development of daily maps that forecast, for the forthcoming week and within federal lands, percentiles of the distributions of (i)...

  16. Modelling and Forecasting Multivariate Realized Volatility

    DEFF Research Database (Denmark)

    Halbleib, Roxana; Voev, Valeri

    2011-01-01

    This paper proposes a methodology for dynamic modelling and forecasting of realized covariance matrices based on fractionally integrated processes. The approach allows for flexible dependence patterns and automatically guarantees positive definiteness of the forecast. We provide an empirical appl...

  17. Interval Forecast for Smooth Transition Autoregressive Model ...

    African Journals Online (AJOL)

    In this paper, we propose a simple method for constructing interval forecast for smooth transition autoregressive (STAR) model. This interval forecast is based on bootstrapping the residual error of the estimated STAR model for each forecast horizon and computing various Akaike information criterion (AIC) function. This new ...

  18. Optimal Power Flow for Distribution Systems under Uncertain Forecasts: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Dall' Anese, Emiliano; Baker, Kyri; Summers, Tyler

    2016-12-01

    The paper focuses on distribution systems featuring renewable energy sources and energy storage devices, and develops an optimal power flow (OPF) approach to optimize the system operation in spite of forecasting errors. The proposed method builds on a chance-constrained multi-period AC OPF formulation, where probabilistic constraints are utilized to enforce voltage regulation with a prescribed probability. To enable a computationally affordable solution approach, a convex reformulation of the OPF task is obtained by resorting to i) pertinent linear approximations of the power flow equations, and ii) convex approximations of the chance constraints. Particularly, the approximate chance constraints provide conservative bounds that hold for arbitrary distributions of the forecasting errors. An adaptive optimization strategy is then obtained by embedding the proposed OPF task into a model predictive control framework.

  19. New interval forecast for stationary autoregressive models ...

    African Journals Online (AJOL)

    In this paper, we proposed a new forecasting interval for stationary Autoregressive, AR(p) models using the Akaike information criterion (AIC) function. Ordinarily, the AIC function is used to determine the order of an AR(p) process. In this study however, AIC forecast interval compared favorably with the theoretical forecast ...

  20. Pollen Forecast and Dispersion Modelling

    Science.gov (United States)

    Costantini, Monica; Di Giuseppe, Fabio; Medaglia, Carlo Maria; Travaglini, Alessandro; Tocci, Raffaella; Brighetti, M. Antonia; Petitta, Marcello

    2014-05-01

    The aim of this study is monitoring, mapping and forecast of pollen distribution for the city of Rome using in-situ measurements of 10 species of common allergenic pollens and measurements of PM10. The production of daily concentration maps, associated to a mobile phone app, are innovative compared to existing dedicated services to people who suffer from respiratory allergies. The dispersal pollen is one of the most well-known causes of allergic disease that is manifested by disorders of the respiratory functions. Allergies are the third leading cause of chronic disease and it is estimated that tens millions of people in Italy suffer from it. Recent works reveal that during the last few years there was a progressive increase of affected subjects, especially in urban areas. This situation may depend: on the ability to transport of pollutants, on the ability to react between pollutants and pollen and from a combination of other irritants, existing in densely populated and polluted urban areas. The methodology used to produce maps is based on in-situ measurements time series relative to 2012, obtained from networks of air quality and pollen stations in the metropolitan area of Rome. The monitoring station aerobiological of University of Rome "Tor Vergata" is located at the Department of Biology. The instrument used to pollen monitoring is a volumetric sampler type Hirst (Hirst 1952), Model 2000 VPPS Lanzoni; the data acquisition is carried out as reported in Standard UNI 11008:2004 - "Qualità dell'aria - Metodo di campionamento e conteggio dei granuli pollinici e delle spore fungine aerodisperse" - the protocol that describes the procedure for measuring of the concentration of pollen grains and fungal spores dispersed into the atmosphere, and reported in the "Manuale di gestione e qualità della R.I.M.A" (Travaglini et. al. 2009). All 10 allergenic pollen are monitored since 1996. At Tor Vergata university is also operating a meteorological station (SP2000, CAE

  1. When mechanism matters: Bayesian forecasting using models of ecological diffusion

    Science.gov (United States)

    Hefley, Trevor J.; Hooten, Mevin B.; Russell, Robin E.; Walsh, Daniel P.; Powell, James A.

    2017-01-01

    Ecological diffusion is a theory that can be used to understand and forecast spatio-temporal processes such as dispersal, invasion, and the spread of disease. Hierarchical Bayesian modelling provides a framework to make statistical inference and probabilistic forecasts, using mechanistic ecological models. To illustrate, we show how hierarchical Bayesian models of ecological diffusion can be implemented for large data sets that are distributed densely across space and time. The hierarchical Bayesian approach is used to understand and forecast the growth and geographic spread in the prevalence of chronic wasting disease in white-tailed deer (Odocoileus virginianus). We compare statistical inference and forecasts from our hierarchical Bayesian model to phenomenological regression-based methods that are commonly used to analyse spatial occurrence data. The mechanistic statistical model based on ecological diffusion led to important ecological insights, obviated a commonly ignored type of collinearity, and was the most accurate method for forecasting.

  2. Uncertainty calculation in transport models and forecasts

    DEFF Research Database (Denmark)

    Manzo, Stefano; Prato, Carlo Giacomo

    Transport projects and policy evaluations are often based on transport model output, i.e. traffic flows and derived effects. However, literature has shown that there is often a considerable difference between forecasted and observed traffic flows. This difference causes misallocation of (public...... implemented by using an approach based on stochastic techniques (Monte Carlo simulation and Bootstrap re-sampling) or scenario analysis combined with model sensitivity tests. Two transport models are used as case studies: the Næstved model and the Danish National Transport Model. 3 The first paper...... in a four-stage transport model related to different variable distributions (to be used in a Monte Carlo simulation procedure), assignment procedures and levels of congestion, at both the link and the network level. The analysis used as case study the Næstved model, referring to the Danish town of Næstved2...

  3. A heuristic forecasting model for stock decision

    OpenAIRE

    Zhang, D.; Jiang, Q.; Li, X.

    2005-01-01

    This paper describes a heuristic forecasting model based on neural networks for stock decision-making. Some heuristic strategies are presented for enhancing the learning capability of neural networks and obtaining better trading performance. The China Shanghai Composite Index is used as case study. The forecasting model can forecast the buying and selling signs according to the result of neural network prediction. Results are compared with a benchmark buy-and-hold strategy. ...

  4. Evaluation Of Statistical Models For Forecast Errors From The HBV-Model

    Science.gov (United States)

    Engeland, K.; Kolberg, S.; Renard, B.; Stensland, I.

    2009-04-01

    Three statistical models for the forecast errors for inflow to the Langvatn reservoir in Northern Norway have been constructed and tested according to how well the distribution and median values of the forecasts errors fit to the observations. For the first model observed and forecasted inflows were transformed by the Box-Cox transformation before a first order autoregressive model was constructed for the forecast errors. The parameters were conditioned on climatic conditions. In the second model the Normal Quantile Transformation (NQT) was applied on observed and forecasted inflows before a similar first order autoregressive model was constructed for the forecast errors. For the last model positive and negative errors were modeled separately. The errors were first NQT-transformed before a model where the mean values were conditioned on climate, forecasted inflow and yesterday's error. To test the three models we applied three criterions: We wanted a) the median values to be close to the observed values; b) the forecast intervals to be narrow; c) the distribution to be correct. The results showed that it is difficult to obtain a correct model for the forecast errors, and that the main challenge is to account for the auto-correlation in the errors. Model 1 and 2 gave similar results, and the main drawback is that the distributions are not correct. The 95% forecast intervals were well identified, but smaller forecast intervals were over-estimated, and larger intervals were under-estimated. Model 3 gave a distribution that fits better, but the median values do not fit well since the auto-correlation is not properly accounted for. If the 95% forecast interval is of interest, Model 2 is recommended. If the whole distribution is of interest, Model 3 is recommended.

  5. Wind Power Forecasting Error Distributions: An International Comparison

    DEFF Research Database (Denmark)

    Hodge, Bri-Mathias; Lew, Debra; Milligan, Michael

    2012-01-01

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

  6. Improving wave forecasting by integrating ensemble modelling and machine learning

    Science.gov (United States)

    O'Donncha, F.; Zhang, Y.; James, S. C.

    2017-12-01

    Modern smart-grid networks use technologies to instantly relay information on supply and demand to support effective decision making. Integration of renewable-energy resources with these systems demands accurate forecasting of energy production (and demand) capacities. For wave-energy converters, this requires wave-condition forecasting to enable estimates of energy production. Current operational wave forecasting systems exhibit substantial errors with wave-height RMSEs of 40 to 60 cm being typical, which limits the reliability of energy-generation predictions thereby impeding integration with the distribution grid. In this study, we integrate physics-based models with statistical learning aggregation techniques that combine forecasts from multiple, independent models into a single "best-estimate" prediction of the true state. The Simulating Waves Nearshore physics-based model is used to compute wind- and currents-augmented waves in the Monterey Bay area. Ensembles are developed based on multiple simulations perturbing input data (wave characteristics supplied at the model boundaries and winds) to the model. A learning-aggregation technique uses past observations and past model forecasts to calculate a weight for each model. The aggregated forecasts are compared to observation data to quantify the performance of the model ensemble and aggregation techniques. The appropriately weighted ensemble model outperforms an individual ensemble member with regard to forecasting wave conditions.

  7. Quasi-static time-series simulation using OpenDSS in IEEE distribution feeder model with high PV penetration and its impact on solar forecasting

    Science.gov (United States)

    Mohammed, Touseef Ahmed Faisal

    Distribution System Simulator developed by Electric Power Research Institute, to simulate grid voltage profile with a large scale PV system under quasi-static time series considering variations of PV output in seconds, minutes, and the average daily load variations. A 13 bus IEEE distribution feeder model is utilized with distributed residential and commercial scale PV at different buses for simulation studies. Time series simulations are discussed for various modes of operation considering dynamic PV penetration at different time periods in a day. In addition, this thesis demonstrates simulations taking into account the presence of moving cloud for solar forecasting studies.

  8. Stochastic model of forecasting spare parts demand

    Directory of Open Access Journals (Sweden)

    Ivan S. Milojević

    2012-01-01

    hypothesis of the existence of phenomenon change trends, the next step in the methodology of forecasting is the determination of a specific growth curve that describes the regularity of the development in time. These curves of growth are obtained by the analytical representation (expression of dynamic lines. There are two basic stages in the process of expression and they are: - The choice of the type of curve the shape of which corresponds to the character of the dynamic order variation - the determination of the number of values (evaluation of the curve parameters. The most widespread method of forecasting is the trend extrapolation. The basis of the trend extrapolation is the continuing of past trends in the future. The simplicity of the trend extrapolation process, on the one hand, and the absence of other information on the other hand, are the main reasons why the trend extrapolation is used for forecasting. The trend extrapolation is founded on the following assumptions: - The phenomenon development can be presented as an evolutionary trajectory or trend, - General conditions that influenced the trend development in the past will not undergo substantial changes in the future. Spare parts demand forecasting is constantly being done in all warehouses, workshops, and at all levels. Without demand forecasting, neither planning nor decision making can be done. Demand forecasting is the input for determining the level of reserve, size of the order, ordering cycles, etc. The question that arises is the one of the reliability and accuracy of a forecast and its effects. Forecasting 'by feeling' is not to be dismissed if there is nothing better, but in this case, one must be prepared for forecasting failures that cause unnecessary accumulation of certain spare parts, and also a chronic shortage of other spare parts. All this significantly increases costs and does not provide a satisfactory supply of spare parts. The main problem of the application of this model is that each

  9. PETRA. The Forecast Model. Synthesis report

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1998-09-01

    The aim of the PETRA project was to develop a model that could recreate the main aspects involved in the demand for travel. The attainment of this objective requires that the model system should retain a high degree of detail and be based on disaggregate models. This was both to ensure an accurate representation of the underlying behavioural intentions, and allow analysis of the underlying travel demand and related aspects across a number of dimensions. This has been achieved in all main respects. The model system is capable of close reproduction of the observed behaviour and generally responds as expected to changes, exhibiting consistent and plausible reactions. The dis-aggregation of the forecast population, according to the various criteria, allows the model to clearly illustrates the behavioural differences between different population segments. Thus, it seems reasonable to conclude that PETRA is capable of detailed analyses of the distributional and behavioural effects of policy changes. (au) EFP-94. 20 refs.

  10. Evaluating Extensions to Coherent Mortality Forecasting Models

    Directory of Open Access Journals (Sweden)

    Syazreen Shair

    2017-03-01

    Full Text Available Coherent models were developed recently to forecast the mortality of two or more sub-populations simultaneously and to ensure long-term non-divergent mortality forecasts of sub-populations. This paper evaluates the forecast accuracy of two recently-published coherent mortality models, the Poisson common factor and the product-ratio functional models. These models are compared to each other and the corresponding independent models, as well as the original Lee–Carter model. All models are applied to age-gender-specific mortality data for Australia and Malaysia and age-gender-ethnicity-specific data for Malaysia. The out-of-sample forecast error of log death rates, male-to-female death rate ratios and life expectancy at birth from each model are compared and examined across groups. The results show that, in terms of overall accuracy, the forecasts of both coherent models are consistently more accurate than those of the independent models for Australia and for Malaysia, but the relative performance differs by forecast horizon. Although the product-ratio functional model outperforms the Poisson common factor model for Australia, the Poisson common factor is more accurate for Malaysia. For the ethnic groups application, ethnic-coherence gives better results than gender-coherence. The results provide evidence that coherent models are preferable to independent models for forecasting sub-populations’ mortality.

  11. Forecasting with periodic autoregressive time series models

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans); R. Paap (Richard)

    1999-01-01

    textabstractThis paper is concerned with forecasting univariate seasonal time series data using periodic autoregressive models. We show how one should account for unit roots and deterministic terms when generating out-of-sample forecasts. We illustrate the models for various quarterly UK consumption

  12. Short term and medium term power distribution load forecasting by neural networks

    International Nuclear Information System (INIS)

    Yalcinoz, T.; Eminoglu, U.

    2005-01-01

    Load forecasting is an important subject for power distribution systems and has been studied from different points of view. In general, load forecasts should be performed over a broad spectrum of time intervals, which could be classified into short term, medium term and long term forecasts. Several research groups have proposed various techniques for either short term load forecasting or medium term load forecasting or long term load forecasting. This paper presents a neural network (NN) model for short term peak load forecasting, short term total load forecasting and medium term monthly load forecasting in power distribution systems. The NN is used to learn the relationships among past, current and future temperatures and loads. The neural network was trained to recognize the peak load of the day, total load of the day and monthly electricity consumption. The suitability of the proposed approach is illustrated through an application to real load shapes from the Turkish Electricity Distribution Corporation (TEDAS) in Nigde. The data represents the daily and monthly electricity consumption in Nigde, Turkey

  13. Coupling meteorological and hydrological models for flood forecasting

    Directory of Open Access Journals (Sweden)

    Bartholmes

    2005-01-01

    Full Text Available This paper deals with the problem of analysing the coupling of meteorological meso-scale quantitative precipitation forecasts with distributed rainfall-runoff models to extend the forecasting horizon. Traditionally, semi-distributed rainfall-runoff models have been used for real time flood forecasting. More recently, increased computer capabilities allow the utilisation of distributed hydrological models with mesh sizes from tenths of metres to a few kilometres. On the other hand, meteorological models, providing the quantitative precipitation forecast, tend to produce average values on meshes ranging from slightly less than 10 to 200 kilometres. Therefore, to improve the quality of flood forecasts, the effects of coupling the meteorological and the hydrological models at different scales were analysed. A distributed hydrological model (TOPKAPI was developed and calibrated using a 1x1 km mesh for the case of the river Po closed at Ponte Spessa (catchment area c. 37000 km2. The model was then coupled with several other European meteorological models ranging from the Limited Area Models (provided by DMI and DWD with resolutions from 0.0625° * 0.0625°, to the ECMWF ensemble predictions with a resolution of 1.85° * 1.85°. Interesting results, describing the coupled model behaviour, are available for a meteorological extreme event in Northern Italy (Nov. 1994. The results demonstrate the poor reliability of the quantitative precipitation forecasts produced by meteorological models presently available; this is not resolved using the Ensemble Forecasting technique, when compared with results obtainable with measured rainfall.

  14. Midway Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Midway Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a suite...

  15. Modelling and forecasting WIG20 daily returns

    DEFF Research Database (Denmark)

    Amado, Cristina; Silvennoinen, Annestiina; Terasvirta, Timo

    of the model is that the deterministic component is specified before estimating the multiplicative conditional variance component. The resulting model is subjected to misspecification tests and its forecasting performance is compared with that of commonly applied models of conditional heteroskedasticity....

  16. Yakutat Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Yakutat, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  17. Bermuda Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Bermuda Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...

  18. Network Bandwidth Utilization Forecast Model on High Bandwidth Network

    Energy Technology Data Exchange (ETDEWEB)

    Yoo, Wucherl; Sim, Alex

    2014-07-07

    With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.

  19. Network bandwidth utilization forecast model on high bandwidth networks

    Energy Technology Data Exchange (ETDEWEB)

    Yoo, Wuchert (William) [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Sim, Alex [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

    2015-03-30

    With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2%. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.

  20. Forecasting Ebola with a regression transmission model

    Directory of Open Access Journals (Sweden)

    Jason Asher

    2018-03-01

    Full Text Available We describe a relatively simple stochastic model of Ebola transmission that was used to produce forecasts with the lowest mean absolute error among Ebola Forecasting Challenge participants. The model enabled prediction of peak incidence, the timing of this peak, and final size of the outbreak. The underlying discrete-time compartmental model used a time-varying reproductive rate modeled as a multiplicative random walk driven by the number of infectious individuals. This structure generalizes traditional Susceptible-Infected-Recovered (SIR disease modeling approaches and allows for the flexible consideration of outbreaks with complex trajectories of disease dynamics. Keywords: Ebola, Forecasting, Mathematical modeling, Bayesian inference

  1. Demand forecast model based on CRM

    Science.gov (United States)

    Cai, Yuancui; Chen, Lichao

    2006-11-01

    With interiorizing day by day management thought that regarding customer as the centre, forecasting customer demand becomes more and more important. In the demand forecast of customer relationship management, the traditional forecast methods have very great limitation because much uncertainty of the demand, these all require new modeling to meet the demands of development. In this paper, the notion is that forecasting the demand according to characteristics of the potential customer, then modeling by it. The model first depicts customer adopting uniform multiple indexes. Secondly, the model acquires characteristic customers on the basis of data warehouse and the technology of data mining. The last, there get the most similar characteristic customer by their comparing and forecast the demands of new customer by the most similar characteristic customer.

  2. Probabilistic Electricity Price Forecasting Models by Aggregation of Competitive Predictors

    Directory of Open Access Journals (Sweden)

    Claudio Monteiro

    2018-04-01

    Full Text Available This article presents original probabilistic price forecasting meta-models (PPFMCP models, by aggregation of competitive predictors, for day-ahead hourly probabilistic price forecasting. The best twenty predictors of the EEM2016 EPF competition are used to create ensembles of hourly spot price forecasts. For each hour, the parameter values of the probability density function (PDF of a Beta distribution for the output variable (hourly price can be directly obtained from the expected and variance values associated to the ensemble for such hour, using three aggregation strategies of predictor forecasts corresponding to three PPFMCP models. A Reliability Indicator (RI and a Loss function Indicator (LI are also introduced to give a measure of uncertainty of probabilistic price forecasts. The three PPFMCP models were satisfactorily applied to the real-world case study of the Iberian Electricity Market (MIBEL. Results from PPFMCP models showed that PPFMCP model 2, which uses aggregation by weight values according to daily ranks of predictors, was the best probabilistic meta-model from a point of view of mean absolute errors, as well as of RI and LI. PPFMCP model 1, which uses the averaging of predictor forecasts, was the second best meta-model. PPFMCP models allow evaluations of risk decisions based on the price to be made.

  3. Econometric Models for Forecasting of Macroeconomic Indices

    Science.gov (United States)

    Sukhanova, Elena I.; Shirnaeva, Svetlana Y.; Mokronosov, Aleksandr G.

    2016-01-01

    The urgency of the research topic was stipulated by the necessity to carry out an effective controlled process by the economic system which can hardly be imagined without indices forecasting characteristic of this system. An econometric model is a safe tool of forecasting which makes it possible to take into consideration the trend of indices…

  4. Forecasting with nonlinear time series models

    DEFF Research Database (Denmark)

    Kock, Anders Bredahl; Teräsvirta, Timo

    In this paper, nonlinear models are restricted to mean nonlinear parametric models. Several such models popular in time series econo- metrics are presented and some of their properties discussed. This in- cludes two models based on universal approximators: the Kolmogorov- Gabor polynomial model...... applied to economic fore- casting problems, is briefly highlighted. A number of large published studies comparing macroeconomic forecasts obtained using different time series models are discussed, and the paper also contains a small simulation study comparing recursive and direct forecasts in a partic...... and two versions of a simple artificial neural network model. Techniques for generating multi-period forecasts from nonlinear models recursively are considered, and the direct (non-recursive) method for this purpose is mentioned as well. Forecasting with com- plex dynamic systems, albeit less frequently...

  5. Forecasting Ebola with a regression transmission model

    OpenAIRE

    Asher, Jason

    2017-01-01

    We describe a relatively simple stochastic model of Ebola transmission that was used to produce forecasts with the lowest mean absolute error among Ebola Forecasting Challenge participants. The model enabled prediction of peak incidence, the timing of this peak, and final size of the outbreak. The underlying discrete-time compartmental model used a time-varying reproductive rate modeled as a multiplicative random walk driven by the number of infectious individuals. This structure generalizes ...

  6. Forecasting distribution of numbers of large fires

    Science.gov (United States)

    Eidenshink, Jeffery C.; Preisler, Haiganoush K.; Howard, Stephen; Burgan, Robert E.

    2014-01-01

    Systems to estimate forest fire potential commonly utilize one or more indexes that relate to expected fire behavior; however they indicate neither the chance that a large fire will occur, nor the expected number of large fires. That is, they do not quantify the probabilistic nature of fire danger. In this work we use large fire occurrence information from the Monitoring Trends in Burn Severity project, and satellite and surface observations of fuel conditions in the form of the Fire Potential Index, to estimate two aspects of fire danger: 1) the probability that a 1 acre ignition will result in a 100+ acre fire, and 2) the probabilities of having at least 1, 2, 3, or 4 large fires within a Predictive Services Area in the forthcoming week. These statistical processes are the main thrust of the paper and are used to produce two daily national forecasts that are available from the U.S. Geological Survey, Earth Resources Observation and Science Center and via the Wildland Fire Assessment System. A validation study of our forecasts for the 2013 fire season demonstrated good agreement between observed and forecasted values.

  7. Combining SKU-level sales forecasts from models and experts

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans); R. Legerstee (Rianne)

    2009-01-01

    textabstractWe study the performance of SKU-level sales forecasts which linearly combine statistical model forecasts and expert forecasts. Using a large and unique database containing model forecasts for monthly sales of various pharmaceutical products and forecasts given by about fifty experts, we

  8. Propagation of Uncertainty in Bayesian Kernel Models - Application to Multiple-Step Ahead Forecasting

    DEFF Research Database (Denmark)

    Quinonero, Joaquin; Girard, Agathe; Larsen, Jan

    2003-01-01

    The object of Bayesian modelling is predictive distribution, which, in a forecasting scenario, enables evaluation of forecasted values and their uncertainties. We focus on reliably estimating the predictive mean and variance of forecasted values using Bayesian kernel based models such as the Gaus......The object of Bayesian modelling is predictive distribution, which, in a forecasting scenario, enables evaluation of forecasted values and their uncertainties. We focus on reliably estimating the predictive mean and variance of forecasted values using Bayesian kernel based models...... such as the Gaussian process and the relevance vector machine. We derive novel analytic expressions for the predictive mean and variance for Gaussian kernel shapes under the assumption of a Gaussian input distribution in the static case, and of a recursive Gaussian predictive density in iterative forecasting...

  9. Nambe Pueblo Water Budget and Forecasting model.

    Energy Technology Data Exchange (ETDEWEB)

    Brainard, James Robert

    2009-10-01

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

  10. Weather forecasting based on hybrid neural model

    Science.gov (United States)

    Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.

    2017-11-01

    Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.

  11. Spatiotemporal drought forecasting using nonlinear models

    Science.gov (United States)

    Vasiliades, Lampros; Loukas, Athanasios

    2010-05-01

    Spatiotemporal data mining is the extraction of unknown and implicit knowledge, structures, spatiotemporal relationships, or patterns not explicitly stored in spatiotemporal databases. As one of data mining techniques, forecasting is widely used to predict the unknown future based upon the patterns hidden in the current and past data. In order to achieve spatiotemporal forecasting, some mature analysis tools, e.g., time series and spatial statistics are extended to the spatial dimension and the temporal dimension, respectively. Drought forecasting plays an important role in the planning and management of natural resources and water resource systems in a river basin. Early and timelines forecasting of a drought event can help to take proactive measures and set out drought mitigation strategies to alleviate the impacts of drought. Despite the widespread application of nonlinear mathematical models, comparative studies on spatiotemporal drought forecasting using different models are still a huge task for modellers. This study uses a promising approach, the Gamma Test (GT), to select the input variables and the training data length, so that the trial and error workload could be greatly reduced. The GT enables to quickly evaluate and estimate the best mean squared error that can be achieved by a smooth model on any unseen data for a given selection of inputs, prior to model construction. The GT is applied to forecast droughts using monthly Standardized Precipitation Index (SPI) timeseries at multiple timescales in several precipitation stations at Pinios river basin in Thessaly region, Greece. Several nonlinear models have been developed efficiently, with the aid of the GT, for 1-month up to 12-month ahead forecasting. Several temporal and spatial statistical indices were considered for the performance evaluation of the models. The predicted results show reasonably good agreement with the actual data for short lead times, whereas the forecasting accuracy decreases with

  12. Multicomponent ensemble models to forecast induced seismicity

    Science.gov (United States)

    Király-Proag, E.; Gischig, V.; Zechar, J. D.; Wiemer, S.

    2018-01-01

    In recent years, human-induced seismicity has become a more and more relevant topic due to its economic and social implications. Several models and approaches have been developed to explain underlying physical processes or forecast induced seismicity. They range from simple statistical models to coupled numerical models incorporating complex physics. We advocate the need for forecast testing as currently the best method for ascertaining if models are capable to reasonably accounting for key physical governing processes—or not. Moreover, operational forecast models are of great interest to help on-site decision-making in projects entailing induced earthquakes. We previously introduced a standardized framework following the guidelines of the Collaboratory for the Study of Earthquake Predictability, the Induced Seismicity Test Bench, to test, validate, and rank induced seismicity models. In this study, we describe how to construct multicomponent ensemble models based on Bayesian weightings that deliver more accurate forecasts than individual models in the case of Basel 2006 and Soultz-sous-Forêts 2004 enhanced geothermal stimulation projects. For this, we examine five calibrated variants of two significantly different model groups: (1) Shapiro and Smoothed Seismicity based on the seismogenic index, simple modified Omori-law-type seismicity decay, and temporally weighted smoothed seismicity; (2) Hydraulics and Seismicity based on numerically modelled pore pressure evolution that triggers seismicity using the Mohr-Coulomb failure criterion. We also demonstrate how the individual and ensemble models would perform as part of an operational Adaptive Traffic Light System. Investigating seismicity forecasts based on a range of potential injection scenarios, we use forecast periods of different durations to compute the occurrence probabilities of seismic events M ≥ 3. We show that in the case of the Basel 2006 geothermal stimulation the models forecast hazardous levels

  13. Modeling and forecasting petroleum futures volatility

    International Nuclear Information System (INIS)

    Sadorsky, Perry

    2006-01-01

    Forecasts of oil price volatility are important inputs into macroeconometric models, financial market risk assessment calculations like value at risk, and option pricing formulas for futures contracts. This paper uses several different univariate and multivariate statistical models to estimate forecasts of daily volatility in petroleum futures price returns. The out-of-sample forecasts are evaluated using forecast accuracy tests and market timing tests. The TGARCH model fits well for heating oil and natural gas volatility and the GARCH model fits well for crude oil and unleaded gasoline volatility. Simple moving average models seem to fit well in some cases provided the correct order is chosen. Despite the increased complexity, models like state space, vector autoregression and bivariate GARCH do not perform as well as the single equation GARCH model. Most models out perform a random walk and there is evidence of market timing. Parametric and non-parametric value at risk measures are calculated and compared. Non-parametric models outperform the parametric models in terms of number of exceedences in backtests. These results are useful for anyone needing forecasts of petroleum futures volatility. (author)

  14. The AviaDem forecasting model: illustration of a forecasting case at Amsterdam Schiphol Airport

    NARCIS (Netherlands)

    Veldhuis, J.; Lieshout, R.

    2010-01-01

    The paper describes an aviation market forecasting model which focuses on market forecasts for airports. Most forecasting models in use today assess aviation trends resulting from macroeconomic trends. The model described in this paper has this feature built in, but the added value of this model is

  15. On the effect of model parameters on forecast objects

    Science.gov (United States)

    Marzban, Caren; Jones, Corinne; Li, Ning; Sandgathe, Scott

    2018-04-01

    Many physics-based numerical models produce a gridded, spatial field of forecasts, e.g., a temperature map. The field for some quantities generally consists of spatially coherent and disconnected objects. Such objects arise in many problems, including precipitation forecasts in atmospheric models, eddy currents in ocean models, and models of forest fires. Certain features of these objects (e.g., location, size, intensity, and shape) are generally of interest. Here, a methodology is developed for assessing the impact of model parameters on the features of forecast objects. The main ingredients of the methodology include the use of (1) Latin hypercube sampling for varying the values of the model parameters, (2) statistical clustering algorithms for identifying objects, (3) multivariate multiple regression for assessing the impact of multiple model parameters on the distribution (across the forecast domain) of object features, and (4) methods for reducing the number of hypothesis tests and controlling the resulting errors. The final output of the methodology is a series of box plots and confidence intervals that visually display the sensitivities. The methodology is demonstrated on precipitation forecasts from a mesoscale numerical weather prediction model.

  16. Forecast errors in dust vertical distributions over Rome (Italy): Multiple particle size representation and cloud contributions

    Science.gov (United States)

    Kishcha, P.; Alpert, P.; Shtivelman, A.; Krichak, S. O.; Joseph, J. H.; Kallos, G.; Katsafados, P.; Spyrou, C.; Gobbi, G. P.; Barnaba, F.; Nickovic, S.; PéRez, C.; Baldasano, J. M.

    2007-08-01

    In this study, forecast errors in dust vertical distributions were analyzed. This was carried out by using quantitative comparisons between dust vertical profiles retrieved from lidar measurements over Rome, Italy, performed from 2001 to 2003, and those predicted by models. Three models were used: the four-particle-size Dust Regional Atmospheric Model (DREAM), the older one-particle-size version of the SKIRON model from the University of Athens (UOA), and the pre-2006 one-particle-size Tel Aviv University (TAU) model. SKIRON and DREAM are initialized on a daily basis using the dust concentration from the previous forecast cycle, while the TAU model initialization is based on the Total Ozone Mapping Spectrometer aerosol index (TOMS AI). The quantitative comparison shows that (1) the use of four-particle-size bins in the dust modeling instead of only one-particle-size bins improves dust forecasts; (2) cloud presence could contribute to noticeable dust forecast errors in SKIRON and DREAM; and (3) as far as the TAU model is concerned, its forecast errors were mainly caused by technical problems with TOMS measurements from the Earth Probe satellite. As a result, dust forecast errors in the TAU model could be significant even under cloudless conditions. The DREAM versus lidar quantitative comparisons at different altitudes show that the model predictions are more accurate in the middle part of dust layers than in the top and bottom parts of dust layers.

  17. Meteoroid Environment Modeling: the Meteoroid Engineering Model and Shower Forecasting

    Science.gov (United States)

    Moorhead, Althea V.

    2017-01-01

    The meteoroid environment is often divided conceptually into meteor showers plus a sporadic background component. The sporadic complex poses the bulk of the risk to spacecraft, but showers can produce significant short-term enhancements of the meteoroid flux. The Meteoroid Environment Office (MEO) has produced two environment models to handle these cases: the Meteoroid Engineering Model (MEM) and an annual meteor shower forecast. Both MEM and the forecast are used by multiple manned spaceflight projects in their meteoroid risk evaluation, and both tools are being revised to incorporate recent meteor velocity, density, and timing measurements. MEM describes the sporadic meteoroid complex and calculates the flux, speed, and directionality of the meteoroid environment relative to a user-supplied spacecraft trajectory, taking the spacecraft's motion into account. MEM is valid in the inner solar system and offers near-Earth and cis-lunar environments. While the current version of MEM offers a nominal meteoroid environment corresponding to a single meteoroid bulk density, the next version of MEMR3 will offer both flux uncertainties and a density distribution in addition to a revised near-Earth environment. We have updated the near-Earth meteor speed distribution and have made the first determination of uncertainty in this distribution. We have also derived a meteor density distribution from the work of Kikwaya et al. (2011). The annual meteor shower forecast takes the form of a report and data tables that can be used in conjunction with an existing MEM assessment. Fluxes are typically quoted to a constant limiting kinetic energy in order to comport with commonly used ballistic limit equations. For the 2017 annual forecast, the MEO substantially revised the list of showers and their characteristics using 14 years of meteor flux measurements from the Canadian Meteor Orbit Radar (CMOR). Defunct or insignificant showers were removed and the temporal profiles of many showers

  18. Inventory model using bayesian dynamic linear model for demand forecasting

    Directory of Open Access Journals (Sweden)

    Marisol Valencia-Cárdenas

    2014-12-01

    Full Text Available An important factor of manufacturing process is the inventory management of terminated product. Constantly, industry is looking for better alternatives to establish an adequate plan of production and stored quantities, with optimal cost, getting quantities in a time horizon, which permits to define resources and logistics with anticipation, needed to distribute products on time. Total absence of historical data, required by many statistical models to forecast, demands the search for other kind of accurate techniques. This work presents an alternative that not only permits to forecast, in an adjusted way, but also, to provide optimal quantities to produce and store with an optimal cost, using Bayesian statistics. The proposal is illustrated with real data. Palabras clave: estadística bayesiana, optimización, modelo de inventarios, modelo lineal dinámico bayesiano. Keywords: Bayesian statistics, opti

  19. Forecasting Inflation through Econometrics Models: An Empirical ...

    African Journals Online (AJOL)

    This article aims at modeling and forecasting inflation in Pakistan. For this purpose a number of econometric approaches are implemented and their results are compared. In ARIMA models, adding additional lags for p and/or q necessarily reduced the sum of squares of the estimated residuals. When a model is estimated ...

  20. Modelling and Forecasting Multivariate Realized Volatility

    DEFF Research Database (Denmark)

    Chiriac, Roxana; Voev, Valeri

    . We provide an empirical application of the model, in which we show by means of stochastic dominance tests that the returns from an optimal portfolio based on the model's forecasts second-order dominate returns of portfolios optimized on the basis of traditional MGARCH models. This result implies...

  1. Forecasting Workload for Defense Logistics Agency Distribution

    Science.gov (United States)

    2014-12-01

    Distribution workload ...........................18 Monthly DD Sales for the four primary supply chains ( Avn , Land, Maritime, Ind HW) plotted to...average AVN Aviation BSM Business Systems Modernization CIT consumable items transfer C&E Construction and Equipment C&T Clothing...992081.437 See Figure 2 below for the graphical output of the linear regression. Monthly DD Sales for the four primary supply chains ( Avn , Land

  2. Enhanced regional forecasting considering single wind farm distribution for upscaling

    International Nuclear Information System (INIS)

    Bremen, Lueder von; Saleck, Nadja; Heinemann, Detlev

    2007-01-01

    With increasing wind power penetration the need for more accurate wind power forecasts increases to raise the market value of wind power. State-of-the-art wind power forecasting tools are considered either statistical or physical. Fundamentally new techniques are rare, thus it is tried to establish a new approach. The spatial decomposition of wind power generation in Germany can be done with principle component analysis to extract the main pattern of variability. They have a physical meaning when linked with typical weather situation. The first four eigenvectors explain about 94 % of the observed variance. The time-evolving principle components are linked with the total wind power feed-in in Germany and are used for its estimation. A new wind power forecasting model has been implemented with this approach and shows very good results that are comparable with state-of-the-art commercial wind power forecast models. The day-ahead forecast error for a common intercomparison period Jan-Jul 2006 is 4.4 %. The suggested approach offers wide ranges for future developments (e.g. several NWP models), because it is computationally very cheap to run

  3. Evaluation of streamflow forecast for the National Water Model of U.S. National Weather Service

    Science.gov (United States)

    Rafieeinasab, A.; McCreight, J. L.; Dugger, A. L.; Gochis, D.; Karsten, L. R.; Zhang, Y.; Cosgrove, B.; Liu, Y.

    2016-12-01

    The National Water Model (NWM), an implementation of the community WRF-Hydro modeling system, is an operational hydrologic forecasting model for the contiguous United States. The model forecasts distributed hydrologic states and fluxes, including soil moisture, snowpack, ET, and ponded water. In particular, the NWM provides streamflow forecasts at more than 2.7 million river reaches for three forecast ranges: short (15 hr), medium (10 days), and long (30 days). In this study, we verify short and medium range streamflow forecasts in the context of the verification of their respective quantitative precipitation forecasts/forcing (QPF), the High Resolution Rapid Refresh (HRRR) and the Global Forecast System (GFS). The streamflow evaluation is performed for summer of 2016 at more than 6,000 USGS gauges. Both individual forecasts and forecast lead times are examined. Selected case studies of extreme events aim to provide insight into the quality of the NWM streamflow forecasts. A goal of this comparison is to address how much streamflow bias originates from precipitation forcing bias. To this end, precipitation verification is performed over the contributing areas above (and between assimilated) USGS gauge locations. Precipitation verification is based on the aggregated, blended StageIV/StageII data as the "reference truth". We summarize the skill of the streamflow forecasts, their skill relative to the QPF, and make recommendations for improving NWM forecast skill.

  4. A Hybrid Model for Forecasting Sales in Turkish Paint Industry

    OpenAIRE

    Alp Ustundag

    2009-01-01

    Sales forecasting is important for facilitating effective and efficient allocation of scarce resources. However, how to best model and forecast sales has been a long-standing issue. There is no best forecasting method that is applicable in all circumstances. Therefore, confidence in the accuracy of sales forecasts is achieved by corroborating the results using two or more methods. This paper proposes a hybrid forecasting model that uses an artificial intelligence method (AI) w...

  5. 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 ...

  6. A mathematical model to forecast uranium production

    International Nuclear Information System (INIS)

    Camisani-Calzolari, F.A.G.M.

    1987-01-01

    The uranium production forecasting program described in this paper projects production from reasonably assured, estimated additional and speculative resources in the cost categories of less than $130/kg U. Originally designed to handle South African production, it has been expanded and redimensioned using available published information to forecast production for countries of the Western World. The program forecasts production from up to 400 plants over a period of fifty years and has built-in production models derived from documented historical data of the more important uranium provinces. It is particularly suitable to assess production capabilities on a national and global scale where variations in outputs for the individual plants tend to even out. The program is aimed at putting the uranium potential of any one country into a realistic perspective, and it could thus be useful for planning purposes and marketing strategies

  7. Forecasting an invasive species’ distribution with global distribution data, local data, and physiological information

    Science.gov (United States)

    Jarnevich, Catherine S.; Young, Nicholas E.; Talbert, Marian; Talbert, Colin

    2018-01-01

    Understanding invasive species distributions and potential invasions often requires broad‐scale information on the environmental tolerances of the species. Further, resource managers are often faced with knowing these broad‐scale relationships as well as nuanced environmental factors related to their landscape that influence where an invasive species occurs and potentially could occur. Using invasive buffelgrass (Cenchrus ciliaris), we developed global models and local models for Saguaro National Park, Arizona, USA, based on location records and literature on physiological tolerances to environmental factors to investigate whether environmental relationships of a species at a global scale are also important at local scales. In addition to correlative models with five commonly used algorithms, we also developed a model using a priori user‐defined relationships between occurrence and environmental characteristics based on a literature review. All correlative models at both scales performed well based on statistical evaluations. The user‐defined curves closely matched those produced by the correlative models, indicating that the correlative models may be capturing mechanisms driving the distribution of buffelgrass. Given climate projections for the region, both global and local models indicate that conditions at Saguaro National Park may become more suitable for buffelgrass. Combining global and local data with correlative models and physiological information provided a holistic approach to forecasting invasive species distributions.

  8. Probabilistic accounting of uncertainty in forecasts of species distributions under climate change

    Science.gov (United States)

    Seth J. Wenger; Nicholas A. Som; Daniel C. Dauwalter; Daniel J. Isaak; Helen M. Neville; Charles H. Luce; Jason B. Dunham; Michael K. Young; Kurt D. Fausch; Bruce E. Rieman

    2013-01-01

    Forecasts of species distributions under future climates are inherently uncertain, but there have been few attempts to describe this uncertainty comprehensively in a probabilistic manner. We developed a Monte Carlo approach that accounts for uncertainty within generalized linear regression models (parameter uncertainty and residual error), uncertainty among competing...

  9. Model-free aftershock forecasts constructed from similar sequences in the past

    Science.gov (United States)

    van der Elst, N.; Page, M. T.

    2017-12-01

    The basic premise behind aftershock forecasting is that sequences in the future will be similar to those in the past. Forecast models typically use empirically tuned parametric distributions to approximate past sequences, and project those distributions into the future to make a forecast. While parametric models do a good job of describing average outcomes, they are not explicitly designed to capture the full range of variability between sequences, and can suffer from over-tuning of the parameters. In particular, parametric forecasts may produce a high rate of "surprises" - sequences that land outside the forecast range. Here we present a non-parametric forecast method that cuts out the parametric "middleman" between training data and forecast. The method is based on finding past sequences that are similar to the target sequence, and evaluating their outcomes. We quantify similarity as the Poisson probability that the observed event count in a past sequence reflects the same underlying intensity as the observed event count in the target sequence. Event counts are defined in terms of differential magnitude relative to the mainshock. The forecast is then constructed from the distribution of past sequences outcomes, weighted by their similarity. We compare the similarity forecast with the Reasenberg and Jones (RJ95) method, for a set of 2807 global aftershock sequences of M≥6 mainshocks. We implement a sequence-specific RJ95 forecast using a global average prior and Bayesian updating, but do not propagate epistemic uncertainty. The RJ95 forecast is somewhat more precise than the similarity forecast: 90% of observed sequences fall within a factor of two of the median RJ95 forecast value, whereas the fraction is 85% for the similarity forecast. However, the surprise rate is much higher for the RJ95 forecast; 10% of observed sequences fall in the upper 2.5% of the (Poissonian) forecast range. The surprise rate is less than 3% for the similarity forecast. The similarity

  10. Forecasting Costa Rican Quarterly Growth with Mixed-frequency Models

    Directory of Open Access Journals (Sweden)

    Adolfo Rodríguez Vargas

    2014-11-01

    Full Text Available We assess the utility of mixed-frequency models to forecast the quarterly growth rate of Costa Rican real GDP: we estimate bridge and MiDaS models with several lag lengths using information of the IMAE and compute forecasts (horizons of 0-4 quarters which are compared between themselves, with those of ARIMA models and with those resulting from forecast combinations. Combining the most accurate forecasts is most useful when forecasting in real time, whereas MiDaS forecasts are the best-performing overall: as the forecasting horizon increases, their precisionis affected relatively little; their success rates in predicting the direction of changes in the growth rate are stable, and several forecastsremain unbiased. In particular, forecasts computed from simple MiDaS with 9 and 12 lags are unbiased at all horizons and information sets assessed, and show the highest number of significant differences in forecasting ability in comparison with all other models.

  11. Sales Forecasting System for Newspaper Distribution Companies in Turkey

    Directory of Open Access Journals (Sweden)

    Gencay İncesu

    2012-07-01

    Full Text Available Normal 0 false false false EN-US X-NONE X-NONE st1\\:*{behavior:url(#ieooui } /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif";} Newspapers are like goods with a shelf life of one day and they have to be distributed daily basis to the sales points. A problem that most newspaper companies encounter daily is how to predict the right number of newspapers to print and distribute among distinct sales points. The aim is to predict newspaper demand as accurately as possible to meet customer need with minimum number of returns, missed sales and oversupply. This makes it necessary to develop a short-term forecasting system. The data taken from one of the largest distribution companies in Turkey is time dependent. Therefore, time series analysis is used to forecast newspaper circulation. In this paper, the newspaper sales system is examined for Turkey. Various types of forecasting techniques which are applicable to newspaper circulation planning are compared and a nonlinear approach for returns is applied.

  12. Mesoscale Modeling, Forecasting and Remote Sensing Research.

    Science.gov (United States)

    remote sensing , cyclonic scale diagnostic studies and mesoscale numerical modeling and forecasting are summarized. Mechanisms involved in the release of potential instability are discussed and simulated quantitatively, giving particular attention to the convective formulation. The basic mesoscale model is documented including the equations, boundary condition, finite differences and initialization through an idealized frontal zone. Results of tests including a three dimensional test with real data, tests of convective/mesoscale interaction and tests with a detailed

  13. Forecasting the Stock Market with Linguistic Rules Generated from the Minimize Entropy Principle and the Cumulative Probability Distribution Approaches

    Directory of Open Access Journals (Sweden)

    Chung-Ho Su

    2010-12-01

    Full Text Available To forecast a complex and non-linear system, such as a stock market, advanced artificial intelligence algorithms, like neural networks (NNs and genetic algorithms (GAs have been proposed as new approaches. However, for the average stock investor, two major disadvantages are argued against these advanced algorithms: (1 the rules generated by NNs and GAs are difficult to apply in investment decisions; and (2 the time complexity of the algorithms to produce forecasting outcomes is very high. Therefore, to provide understandable rules for investors and to reduce the time complexity of forecasting algorithms, this paper proposes a novel model for the forecasting process, which combines two granulating methods (the minimize entropy principle approach and the cumulative probability distribution approach and a rough set algorithm. The model verification demonstrates that the proposed model surpasses the three listed conventional fuzzy time-series models and a multiple regression model (MLR in forecast accuracy.

  14. EXPENSES FORECASTING MODEL IN UNIVERSITY PROJECTS PLANNING

    Directory of Open Access Journals (Sweden)

    Sergei A. Arustamov

    2016-11-01

    Full Text Available The paper deals with mathematical model presentation of cash flows in project funding. We describe different types of expenses linked to university project activities. Problems of project budgeting that contribute most uncertainty have been revealed. As an example of the model implementation we consider calculation of vacation allowance expenses for project participants. We define problems of forecast for funds reservation: calculation based on methodology established by the Ministry of Education and Science calculation according to the vacation schedule and prediction of the most probable amount. A stochastic model for vacation allowance expenses has been developed. We have proposed methods and solution of the problems that increase the accuracy of forecasting for funds reservation based on 2015 data.

  15. Weather Research and Forecasting (WRF) Regional Atmospheric Model: Samoa

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the islands of Samoa at...

  16. Weather Research and Forecasting (WRF) Regional Atmospheric Model: Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the island of Guam at...

  17. Weather Research and Forecasting (WRF) Regional Atmospheric Model: Oahu

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 3.5-day hourly forecast for the region surrounding the Hawaiian island of Oahu at...

  18. Weather Research and Forecasting (WRF) Regional Atmospheric Model: CNMI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the Commonwealth of the Northern...

  19. AIRLINE ACTIVITY FORECASTING BY REGRESSION MODELS

    Directory of Open Access Journals (Sweden)

    Н. Білак

    2012-04-01

    Full Text Available Proposed linear and nonlinear regression models, which take into account the equation of trend and seasonality indices for the analysis and restore the volume of passenger traffic over the past period of time and its prediction for future years, as well as the algorithm of formation of these models based on statistical analysis over the years. The desired model is the first step for the synthesis of more complex models, which will enable forecasting of passenger (income level airline with the highest accuracy and time urgency.

  20. Modeling and Forecasting Large Realized Covariance Matrices and Portfolio Choice

    NARCIS (Netherlands)

    Callot, Laurent A.F.; Kock, Anders B.; Medeiros, Marcelo C.

    2017-01-01

    We consider modeling and forecasting large realized covariance matrices by penalized vector autoregressive models. We consider Lasso-type estimators to reduce the dimensionality and provide strong theoretical guarantees on the forecast capability of our procedure. We show that we can forecast

  1. forecasting with nonlinear time series model: a monte-carlo

    African Journals Online (AJOL)

    PUBLICATIONS1

    erated recursively up to any step greater than one. For nonlinear time series model, point forecast for step one can be done easily like in the linear case but forecast for a step greater than or equal to ..... London. Franses, P. H. (1998). Time series models for business and Economic forecasting, Cam- bridge University press.

  2. The Red Sea Modeling and Forecasting System

    KAUST Repository

    Hoteit, Ibrahim

    2015-04-01

    Despite its importance for a variety of socio-economical and political reasons and the presence of extensive coral reef gardens along its shores, the Red Sea remains one of the most under-studied large marine physical and biological systems in the global ocean. This contribution will present our efforts to build advanced modeling and forecasting capabilities for the Red Sea, which is part of the newly established Saudi ARAMCO Marine Environmental Research Center at KAUST (SAMERCK). Our Red Sea modeling system compromises both regional and nested costal MIT general circulation models (MITgcm) with resolutions varying between 8 km and 250 m to simulate the general circulation and mesoscale dynamics at various spatial scales, a 10-km resolution Weather Research Forecasting (WRF) model to simulate the atmospheric conditions, a 4-km resolution European Regional Seas Ecosystem Model (ERSEM) to simulate the Red Sea ecosystem, and a 1-km resolution WAVEWATCH-III model to simulate the wind driven surface waves conditions. We have also implemented an oil spill model, and a probabilistic dispersion and larval connectivity modeling system (CMS) based on a stochastic Lagrangian framework and incorporating biological attributes. We are using the models outputs together with available observational data to study all aspects of the Red Sea circulations. Advanced monitoring capabilities are being deployed in the Red Sea as part of the SAMERCK, comprising multiple gliders equipped with hydrographical and biological sensors, high frequency (HF) surface current/wave mapping, buoys/ moorings, etc, complementing the available satellite ocean and atmospheric observations and Automatic Weather Stations (AWS). The Red Sea models have also been equipped with advanced data assimilation capabilities. Fully parallel ensemble-based Kalman filtering (EnKF) algorithms have been implemented with the MITgcm and ERSEM for assimilating all available multivariate satellite and in-situ data sets. We

  3. The Red Sea Modeling and Forecasting System

    KAUST Repository

    Hoteit, Ibrahim; Gopalakrishnan, Ganesh; Latif, Hatem; Toye, Habib; Zhan, Peng; Kartadikaria, Aditya R.; Viswanadhapalli, Yesubabu; Yao, Fengchao; Triantafyllou, George; Langodan, Sabique; Cavaleri, Luigi; Guo, Daquan; Johns, Burt

    2015-01-01

    Despite its importance for a variety of socio-economical and political reasons and the presence of extensive coral reef gardens along its shores, the Red Sea remains one of the most under-studied large marine physical and biological systems in the global ocean. This contribution will present our efforts to build advanced modeling and forecasting capabilities for the Red Sea, which is part of the newly established Saudi ARAMCO Marine Environmental Research Center at KAUST (SAMERCK). Our Red Sea modeling system compromises both regional and nested costal MIT general circulation models (MITgcm) with resolutions varying between 8 km and 250 m to simulate the general circulation and mesoscale dynamics at various spatial scales, a 10-km resolution Weather Research Forecasting (WRF) model to simulate the atmospheric conditions, a 4-km resolution European Regional Seas Ecosystem Model (ERSEM) to simulate the Red Sea ecosystem, and a 1-km resolution WAVEWATCH-III model to simulate the wind driven surface waves conditions. We have also implemented an oil spill model, and a probabilistic dispersion and larval connectivity modeling system (CMS) based on a stochastic Lagrangian framework and incorporating biological attributes. We are using the models outputs together with available observational data to study all aspects of the Red Sea circulations. Advanced monitoring capabilities are being deployed in the Red Sea as part of the SAMERCK, comprising multiple gliders equipped with hydrographical and biological sensors, high frequency (HF) surface current/wave mapping, buoys/ moorings, etc, complementing the available satellite ocean and atmospheric observations and Automatic Weather Stations (AWS). The Red Sea models have also been equipped with advanced data assimilation capabilities. Fully parallel ensemble-based Kalman filtering (EnKF) algorithms have been implemented with the MITgcm and ERSEM for assimilating all available multivariate satellite and in-situ data sets. We

  4. Development of a sales forecasting model for canopy windows

    OpenAIRE

    2014-01-01

    M.Com. (Business Management) Forecasting is an important function used in a wide range of business planning or decision-making situations. The purpose ofthis study was to build a sales forecasting model that would be practical and cost effective, from the various forecasting methods and techniques available. Various forecast models, methods and techniques are outlined in the initial part of this study by the author. The author has outlined some of the fundamentals and limitations that unde...

  5. Towards operational modeling and forecasting of the Iberian shelves ecosystem.

    Directory of Open Access Journals (Sweden)

    Martinho Marta-Almeida

    Full Text Available There is a growing interest on physical and biogeochemical oceanic hindcasts and forecasts from a wide range of users and businesses. In this contribution we present an operational biogeochemical forecast system for the Portuguese and Galician oceanographic regions, where atmospheric, hydrodynamic and biogeochemical variables are integrated. The ocean model ROMS, with a horizontal resolution of 3 km, is forced by the atmospheric model WRF and includes a Nutrients-Phytoplankton-Zooplankton-Detritus biogeochemical module (NPZD. In addition to oceanographic variables, the system predicts the concentration of nitrate, phytoplankton, zooplankton and detritus (mmol N m(-3. Model results are compared against radar currents and remote sensed SST and chlorophyll. Quantitative skill assessment during a summer upwelling period shows that our modelling system adequately represents the surface circulation over the shelf including the observed spatial variability and trends of temperature and chlorophyll concentration. Additionally, the skill assessment also shows some deficiencies like the overestimation of upwelling circulation and consequently, of the duration and intensity of the phytoplankton blooms. These and other departures from the observations are discussed, their origins identified and future improvements suggested. The forecast system is the first of its kind in the region and provides free online distribution of model input and output, as well as comparisons of model results with satellite imagery for qualitative operational assessment of model skill.

  6. 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...

  7. Real-time Social Internet Data to Guide Forecasting Models

    Energy Technology Data Exchange (ETDEWEB)

    Del Valle, Sara Y. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-09-20

    Our goal is to improve decision support by monitoring and forecasting events using social media, mathematical models, and quantifying model uncertainty. Our approach is real-time, data-driven forecasts with quantified uncertainty: Not just for weather anymore. Information flow from human observations of events through an Internet system and classification algorithms is used to produce quantitatively uncertain forecast. In summary, we want to develop new tools to extract useful information from Internet data streams, develop new approaches to assimilate real-time information into predictive models, validate approaches by forecasting events, and our ultimate goal is to develop an event forecasting system using mathematical approaches and heterogeneous data streams.

  8. Short term load forecasting: two stage modelling

    Directory of Open Access Journals (Sweden)

    SOARES, L. J.

    2009-06-01

    Full Text Available This paper studies the hourly electricity load demand in the area covered by a utility situated in the Seattle, USA, called Puget Sound Power and Light Company. Our proposal is put into proof with the famous dataset from this company. We propose a stochastic model which employs ANN (Artificial Neural Networks to model short-run dynamics and the dependence among adjacent hours. The model proposed treats each hour's load separately as individual single series. This approach avoids modeling the intricate intra-day pattern (load profile displayed by the load, which varies throughout days of the week and seasons. The forecasting performance of the model is evaluated in similiar mode a TLSAR (Two-Level Seasonal Autoregressive model proposed by Soares (2003 using the years of 1995 and 1996 as the holdout sample. Moreover, we conclude that non linearity is present in some series of these data. The model results are analyzed. The experiment shows that our tool can be used to produce load forecasting in tropical climate places.

  9. Forecasting oil price trends using wavelets and hidden Markov models

    International Nuclear Information System (INIS)

    Souza e Silva, Edmundo G. de; Souza e Silva, Edmundo A. de; Legey, Luiz F.L.

    2010-01-01

    The crude oil price is influenced by a great number of factors, most of which interact in very complex ways. For this reason, forecasting it through a fundamentalist approach is a difficult task. An alternative is to use time series methodologies, with which the price's past behavior is conveniently analyzed, and used to predict future movements. In this paper, we investigate the usefulness of a nonlinear time series model, known as hidden Markov model (HMM), to predict future crude oil price movements. Using an HMM, we develop a forecasting methodology that consists of, basically, three steps. First, we employ wavelet analysis to remove high frequency price movements, which can be assumed as noise. Then, the HMM is used to forecast the probability distribution of the price return accumulated over the next F days. Finally, from this distribution, we infer future price trends. Our results indicate that the proposed methodology might be a useful decision support tool for agents participating in the crude oil market. (author)

  10. Modeling, Forecasting and Mitigating Extreme Earthquakes

    Science.gov (United States)

    Ismail-Zadeh, A.; Le Mouel, J.; Soloviev, A.

    2012-12-01

    Recent earthquake disasters highlighted the importance of multi- and trans-disciplinary studies of earthquake risk. A major component of earthquake disaster risk analysis is hazards research, which should cover not only a traditional assessment of ground shaking, but also studies of geodetic, paleoseismic, geomagnetic, hydrological, deep drilling and other geophysical and geological observations together with comprehensive modeling of earthquakes and forecasting extreme events. Extreme earthquakes (large magnitude and rare events) are manifestations of complex behavior of the lithosphere structured as a hierarchical system of blocks of different sizes. Understanding of physics and dynamics of the extreme events comes from observations, measurements and modeling. A quantitative approach to simulate earthquakes in models of fault dynamics will be presented. The models reproduce basic features of the observed seismicity (e.g., the frequency-magnitude relationship, clustering of earthquakes, occurrence of extreme seismic events). They provide a link between geodynamic processes and seismicity, allow studying extreme events, influence of fault network properties on seismic patterns and seismic cycles, and assist, in a broader sense, in earthquake forecast modeling. Some aspects of predictability of large earthquakes (how well can large earthquakes be predicted today?) will be also discussed along with possibilities in mitigation of earthquake disasters (e.g., on 'inverse' forensic investigations of earthquake disasters).

  11. Daily Peak Load Forecasting of Next Day using Weather Distribution and Comparison Value of Each Nearby Date Data

    Science.gov (United States)

    Ito, Shigenobu; Yukita, Kazuto; Goto, Yasuyuki; Ichiyanagi, Katsuhiro; Nakano, Hiroyuki

    By the development of industry, in recent years; dependence to electric energy is growing year by year. Therefore, reliable electric power supply is in need. However, to stock a huge amount of electric energy is very difficult. Also, there is a necessity to keep balance between the demand and supply, which changes hour after hour. Consequently, to supply the high quality and highly dependable electric power supply, economically, and with high efficiency, there is a need to forecast the movement of the electric power demand carefully in advance. And using that forecast as the source, supply and demand management plan should be made. Thus load forecasting is said to be an important job among demand investment of electric power companies. So far, forecasting method using Fuzzy logic, Neural Net Work, Regression model has been suggested for the development of forecasting accuracy. Those forecasting accuracy is in a high level. But to invest electric power in higher accuracy more economically, a new forecasting method with higher accuracy is needed. In this paper, to develop the forecasting accuracy of the former methods, the daily peak load forecasting method using the weather distribution of highest and lowest temperatures, and comparison value of each nearby date data is suggested.

  12. Neural Network Models for Time Series Forecasts

    OpenAIRE

    Tim Hill; Marcus O'Connor; William Remus

    1996-01-01

    Neural networks have been advocated as an alternative to traditional statistical forecasting methods. In the present experiment, time series forecasts produced by neural networks are compared with forecasts from six statistical time series methods generated in a major forecasting competition (Makridakis et al. [Makridakis, S., A. Anderson, R. Carbone, R. Fildes, M. Hibon, R. Lewandowski, J. Newton, E. Parzen, R. Winkler. 1982. The accuracy of extrapolation (time series) methods: Results of a ...

  13. Space weather: Modeling and forecasting ionospheric

    International Nuclear Information System (INIS)

    Calzadilla Mendez, A.

    2008-01-01

    Full text: Space weather is the set of phenomena and interactions that take place in the interplanetary medium. It is regulated primarily by the activity originating in the Sun and affects both the artificial satellites that are outside of the protective cover of the Earth's atmosphere as the rest of the planets in the solar system. Among the phenomena that are of great relevance and impact on Earth are the auroras and geomagnetic storms , these are a direct result of irregularities in the flow of the solar wind and the interplanetary magnetic field . Given the high complexity of the physical phenomena involved (magnetic reconnection , particle inlet and ionizing radiation to the atmosphere) one of the great scientific challenges today is to forecast the state of plasmatic means either the interplanetary medium , the magnetosphere and ionosphere , for their importance to the development of various human activities such as radio , global positioning , navigation, etc. . It briefly address some of the international ionospheric modeling methods and contributions and participation that currently has the space group of the Institute of Geophysics Geophysics and Astronomy (IGA) in these activities of modeling and forecasting ionospheric. (author)

  14. Pharmaceutical expenditure forecast model to support health policy decision making.

    Science.gov (United States)

    Rémuzat, Cécile; Urbinati, Duccio; Kornfeld, Åsa; Vataire, Anne-Lise; Cetinsoy, Laurent; Aballéa, Samuel; Mzoughi, Olfa; Toumi, Mondher

    2014-01-01

    With constant incentives for healthcare payers to contain their pharmaceutical budgets, modelling policy decision impact became critical. The objective of this project was to test the impact of various policy decisions on pharmaceutical budget (developed for the European Commission for the project 'European Union (EU) Pharmaceutical expenditure forecast' - http://ec.europa.eu/health/healthcare/key_documents/index_en.htm). A model was built to assess policy scenarios' impact on the pharmaceutical budgets of seven member states of the EU, namely France, Germany, Greece, Hungary, Poland, Portugal, and the United Kingdom. The following scenarios were tested: expanding the UK policies to EU, changing time to market access, modifying generic price and penetration, shifting the distribution chain of biosimilars (retail/hospital). Applying the UK policy resulted in dramatic savings for Germany (10 times the base case forecast) and substantial additional savings for France and Portugal (2 and 4 times the base case forecast, respectively). Delaying time to market was found be to a very powerful tool to reduce pharmaceutical expenditure. Applying the EU transparency directive (6-month process for pricing and reimbursement) increased pharmaceutical expenditure for all countries (from 1.1 to 4 times the base case forecast), except in Germany (additional savings). Decreasing the price of generics and boosting the penetration rate, as well as shifting distribution of biosimilars through hospital chain were also key methods to reduce pharmaceutical expenditure. Change in the level of reimbursement rate to 100% in all countries led to an important increase in the pharmaceutical budget. Forecasting pharmaceutical expenditure is a critical exercise to inform policy decision makers. The most important leverages identified by the model on pharmaceutical budget were driven by generic and biosimilar prices, penetration rate, and distribution. Reducing, even slightly, the prices of

  15. Introducing distributed learning approaches in wind power forecasting

    DEFF Research Database (Denmark)

    Pinson, Pierre

    2016-01-01

    Renewable energy forecasting is now of core interest to both academics, who continuously propose new forecast methodologies, and forecast users for optimal operations and participation in electricity markets. In view of the increasing amount of data being collected at power generation sites, thanks...

  16. INFERENCE AND SENSITIVITY IN STOCHASTIC WIND POWER FORECAST MODELS.

    KAUST Repository

    Elkantassi, Soumaya

    2017-10-03

    Reliable forecasting of wind power generation is crucial to optimal control of costs in generation of electricity with respect to the electricity demand. Here, we propose and analyze stochastic wind power forecast models described by parametrized stochastic differential equations, which introduce appropriate fluctuations in numerical forecast outputs. We use an approximate maximum likelihood method to infer the model parameters taking into account the time correlated sets of data. Furthermore, we study the validity and sensitivity of the parameters for each model. We applied our models to Uruguayan wind power production as determined by historical data and corresponding numerical forecasts for the period of March 1 to May 31, 2016.

  17. INFERENCE AND SENSITIVITY IN STOCHASTIC WIND POWER FORECAST MODELS.

    KAUST Repository

    Elkantassi, Soumaya; Kalligiannaki, Evangelia; Tempone, Raul

    2017-01-01

    Reliable forecasting of wind power generation is crucial to optimal control of costs in generation of electricity with respect to the electricity demand. Here, we propose and analyze stochastic wind power forecast models described by parametrized stochastic differential equations, which introduce appropriate fluctuations in numerical forecast outputs. We use an approximate maximum likelihood method to infer the model parameters taking into account the time correlated sets of data. Furthermore, we study the validity and sensitivity of the parameters for each model. We applied our models to Uruguayan wind power production as determined by historical data and corresponding numerical forecasts for the period of March 1 to May 31, 2016.

  18. Neural network versus classical time series forecasting models

    Science.gov (United States)

    Nor, Maria Elena; Safuan, Hamizah Mohd; Shab, Noorzehan Fazahiyah Md; Asrul, Mohd; Abdullah, Affendi; Mohamad, Nurul Asmaa Izzati; Lee, Muhammad Hisyam

    2017-05-01

    Artificial neural network (ANN) has advantage in time series forecasting as it has potential to solve complex forecasting problems. This is because ANN is data driven approach which able to be trained to map past values of a time series. In this study the forecast performance between neural network and classical time series forecasting method namely seasonal autoregressive integrated moving average models was being compared by utilizing gold price data. Moreover, the effect of different data preprocessing on the forecast performance of neural network being examined. The forecast accuracy was evaluated using mean absolute deviation, root mean square error and mean absolute percentage error. It was found that ANN produced the most accurate forecast when Box-Cox transformation was used as data preprocessing.

  19. Electricity price forecasting through transfer function models

    International Nuclear Information System (INIS)

    Nogales, F.J.; Conejo, A.J.

    2006-01-01

    Forecasting electricity prices in present day competitive electricity markets is a must for both producers and consumers because both need price estimates to develop their respective market bidding strategies. This paper proposes a transfer function model to predict electricity prices based on both past electricity prices and demands, and discuss the rationale to build it. The importance of electricity demand information is assessed. Appropriate metrics to appraise prediction quality are identified and used. Realistic and extensive simulations based on data from the PJM Interconnection for year 2003 are conducted. The proposed model is compared with naive and other techniques. Journal of the Operational Research Society (2006) 57, 350-356.doi:10.1057/palgrave.jors.2601995; published online 18 May 2005. (author)

  20. Statistical and RBF NN models : providing forecasts and risk assessment

    OpenAIRE

    Marček, Milan

    2009-01-01

    Forecast accuracy of economic and financial processes is a popular measure for quantifying the risk in decision making. In this paper, we develop forecasting models based on statistical (stochastic) methods, sometimes called hard computing, and on a soft method using granular computing. We consider the accuracy of forecasting models as a measure for risk evaluation. It is found that the risk estimation process based on soft methods is simplified and less critical to the question w...

  1. Tourism Demand Modelling and Forecasting: A Review of Recent Research

    OpenAIRE

    Song, H; Li, G

    2008-01-01

    This paper reviews the published studies on tourism demand modelling and forecasting since 2000. One of the key findings of this review is that the methods used in analysing and forecasting the demand for tourism have been more diverse than those identified by other review articles. In addition to the most popular time-series and econometric models, a number of new techniques have emerged in the literature. However, as far as the forecasting accuracy is concerned, the study shows that there i...

  2. Data Driven Broiler Weight Forecasting using Dynamic Neural Network Models

    DEFF Research Database (Denmark)

    Johansen, Simon Vestergaard; Bendtsen, Jan Dimon; Riisgaard-Jensen, Martin

    2017-01-01

    In this article, the dynamic influence of environmental broiler house conditions and broiler growth is investigated. Dynamic neural network forecasting models have been trained on farm-scale broiler batch production data from 12 batches from the same house. The model forecasts future broiler weight...... and uses environmental conditions such as heating, ventilation, and temperature along with broiler behavior such as feed and water consumption. Training data and forecasting data is analyzed to explain when the model might fail at generalizing. We present ensemble broiler weight forecasts to day 7, 14, 21...

  3. Models of Investor Forecasting Behavior — Experimental Evidence

    Directory of Open Access Journals (Sweden)

    Federico Bonetto

    2017-12-01

    Full Text Available Different forecasting behaviors affect investors’ trading decisions and lead to qualitatively different asset price trajectories. It has been shown in the literature that the weights that investors place on observed asset price changes when forecasting future price changes, and the nature of their confidence when price changes are forecast, determine whether price bubbles, price crashes, and unpredictable price cycles occur. In this paper, we report the results of behavioral experiments involving multiple investors who participated in a market for a virtual asset. Our goal is to study investors’ forecast formation. We conducted three experimental sessions with different participants in each session. We fit different models of forecast formation to the observed data. There is strong evidence that the investors forecast future prices by extrapolating past price changes, even when they know the fundamental value of the asset exactly and the extrapolated forecasts differ significantly from the fundamental value. The rational expectations hypothesis seems inconsistent with the observed forecasts. The forecasting models of all participants that best fit the observed forecasting data were of the type that cause price bubbles and cycles in dynamical systems models, and price bubbles and cycles ended up occurring in all three sessions.

  4. A Hybrid Model for Forecasting Sales in Turkish Paint Industry

    Directory of Open Access Journals (Sweden)

    Alp Ustundag

    2009-12-01

    Full Text Available Sales forecasting is important for facilitating effective and efficient allocation of scarce resources. However, how to best model and forecast sales has been a long-standing issue. There is no best forecasting method that is applicable in all circumstances. Therefore, confidence in the accuracy of sales forecasts is achieved by corroborating the results using two or more methods. This paper proposes a hybrid forecasting model that uses an artificial intelligence method (AI with multiple linear regression (MLR to predict product sales for the largest Turkish paint producer. In the hybrid model, three different AI methods, fuzzy rule-based system (FRBS, artificial neural network (ANN and adaptive neuro fuzzy network (ANFIS, are used and compared to each other. The results indicate that FRBS yields better forecasting accuracy in terms of root mean squared error (RMSE and mean absolute percentage error (MAPE.

  5. Continuous hydrological modelling in the context of real time flood forecasting in alpine Danube tributary catchments

    International Nuclear Information System (INIS)

    Stanzel, Ph; Kahl, B; Haberl, U; Herrnegger, M; Nachtnebel, H P

    2008-01-01

    A hydrological modelling framework applied within operational flood forecasting systems in three alpine Danube tributary basins, Traisen, Salzach and Enns, is presented. A continuous, semi-distributed rainfall-runoff model, accounting for the main hydrological processes of snow accumulation and melt, interception, evapotranspiration, infiltration, runoff generation and routing is set up. Spatial discretization relies on the division of watersheds into subbasins and subsequently into hydrologic response units based on spatial information on soil types, land cover and elevation bands. The hydrological models are calibrated with meteorological ground measurements and with meteorological analyses incorporating radar information. Operationally, each forecasting sequence starts with the re-calculation of the last 24 to 48 hours. Errors between simulated and observed runoff are minimized by optimizing a correction factor for the input to provide improved system states. For the hydrological forecast quantitative 48 or 72 hour forecast grids of temperature and precipitation - deterministic and probabilistic - are used as input. The forecasted hydrograph is corrected with an autoregressive model. The forecasting sequences are repeated each 15 minutes. First evaluations of resulting hydrological forecasts are presented and reliability of forecasts with different lead times is discussed.

  6. Changes in forecasting of HV/MV transformer loading due to distributed generation

    NARCIS (Netherlands)

    Berende, M.J.C.; Ruiter, de A.; Morren, J.

    2013-01-01

    This paper describes how Enexis, one of the largest distribution network operators in the Netherlands, has adapted its load forecasting method for HV/MV-transformers to incorporate the influence of distributed generation. This new method involves the making of separate forecasts for demand and

  7. Analysis and forecast of electrical distribution system materials. Final report. Volume III. Appendix

    Energy Technology Data Exchange (ETDEWEB)

    Love, C G

    1976-08-23

    These appendixes are referenced in Volume II of this report. They contain the detailed electrical distribution equipment requirements and input material requirements forecasts. Forecasts are given for three electric energy usage scenarios. Also included are data on worldwide reserves and demand for 30 raw materials required for the manufacture of electrical distribution equipment.

  8. A Simple Hybrid Model for Short-Term Load Forecasting

    Directory of Open Access Journals (Sweden)

    Suseelatha Annamareddi

    2013-01-01

    Full Text Available The paper proposes a simple hybrid model to forecast the electrical load data based on the wavelet transform technique and double exponential smoothing. The historical noisy load series data is decomposed into deterministic and fluctuation components using suitable wavelet coefficient thresholds and wavelet reconstruction method. The variation characteristics of the resulting series are analyzed to arrive at reasonable thresholds that yield good denoising results. The constitutive series are then forecasted using appropriate exponential adaptive smoothing models. A case study performed on California energy market data demonstrates that the proposed method can offer high forecasting precision for very short-term forecasts, considering a time horizon of two weeks.

  9. Forecasting Multivariate Volatility using the VARFIMA Model on Realized Covariance Cholesky Factors

    DEFF Research Database (Denmark)

    Halbleib, Roxana; Voev, Valeri

    2011-01-01

    This paper analyzes the forecast accuracy of the multivariate realized volatility model introduced by Chiriac and Voev (2010), subject to different degrees of model parametrization and economic evaluation criteria. Bymodelling the Cholesky factors of the covariancematrices, the model generates......, regardless of the type of utility function or return distribution, would be better-off from using this model than from using some standard approaches....

  10. Nawiliwili, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Nawiliwili, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  11. Neah Bay, Washington Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Neah Bay, Washington Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  12. Bar Harbor, ME Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Bar Harbor, Maine Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  13. Sitka, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Sitka, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is...

  14. Newport, Oregon Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Newport, Oregon Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  15. Wake Island Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Wake Island Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...

  16. Garibaldi, Oregon Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Garibaldi, Oregon Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  17. Keauhou, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Keauhou, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  18. Westport, Washington Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Westport, Washington Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  19. Daytona Beach, Florida Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Daytona Beach, Florida Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  20. Lahaina, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Lahaina, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  1. Fajardo, Puerto Rico Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Fajardo, Puerto Rico Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  2. Florence, Oregon Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Florence, Oregon Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  3. Ponce, Puerto Rico Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Ponce, Puerto Rico Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  4. Shemya, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Shemya, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is...

  5. Key West, Florida Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Key West, Florida Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  6. Montauk, New York Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Montauk, New York Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  7. Kodiak, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Kodiak, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is...

  8. Sand Point, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Sand Point, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  9. Ocean City, Maryland Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Ocean City, Maryland Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  10. Kahului, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Kahului, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  11. Port Orford, Oregon Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Port Orford, Oregon Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  12. Kailua-Kona, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Kailua-Kona, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  13. Seward, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Seward, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is...

  14. Seaside, Oregon Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Seaside, Oregon Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  15. Apra Harbor, Guam Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Apra Harbor, Guam Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  16. Kihei, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Kihei, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is...

  17. Adak, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Adak, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...

  18. Arecibo, Puerto Rico Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Arecibo, Puerto Rico Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  19. San Juan, Puerto Rico Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The San Juan, Puerto Rico Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  20. Pearl Harbor, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Pearl Harbor, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  1. Palm Beach, Florida Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Palm Beach, Florida Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  2. Toke Point, Washington Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Toke Point, Washington Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  3. Hanalei, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Hanalei, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  4. Homer, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Homer, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is...

  5. Nikolski, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Nikolski, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  6. Monterey, California Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Monterey, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  7. Port Alexander, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Port Alexander, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  8. La Push, Washington Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The La Push, Washington Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  9. Haleiwa, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Haleiwa, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  10. Hilo, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Hilo, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...

  11. Savannah, Georgia Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Savannah, Georgia Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  12. Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection

    DEFF Research Database (Denmark)

    Bork, Lasse; Møller, Stig Vinther

    2015-01-01

    We examine house price forecastability across the 50 states using Dynamic Model Averaging and Dynamic Model Selection, which allow for model change and parameter shifts. By allowing the entire forecasting model to change over time and across locations, the forecasting accuracy improves substantia......We examine house price forecastability across the 50 states using Dynamic Model Averaging and Dynamic Model Selection, which allow for model change and parameter shifts. By allowing the entire forecasting model to change over time and across locations, the forecasting accuracy improves...

  13. Day ahead price forecasting of electricity markets by a mixed data model and hybrid forecast method

    International Nuclear Information System (INIS)

    Amjady, Nima; Keynia, Farshid

    2008-01-01

    In a competitive electricity market, forecast of energy prices is a key information for the market participants. However, price signal usually has a complex behavior due to its nonlinearity, nonstationarity, and time variancy. In spite of all performed researches on this area in the recent years, there is still an essential need for more accurate and robust price forecast methods. In this paper, a combination of wavelet transform (WT) and a hybrid forecast method is proposed for this purpose. The hybrid method is composed of cascaded forecasters where each forecaster consists of a neural network (NN) and an evolutionary algorithms (EA). Both time domain and wavelet domain features are considered in a mixed data model for price forecast, in which the candidate input variables are refined by a feature selection technique. The adjustable parameters of the whole method are fine-tuned by a cross-validation technique. The proposed method is examined on PJM electricity market and compared with some of the most recent price forecast methods. (author)

  14. Pharmaceutical expenditure forecast model to support health policy decision making

    Science.gov (United States)

    Rémuzat, Cécile; Urbinati, Duccio; Kornfeld, Åsa; Vataire, Anne-Lise; Cetinsoy, Laurent; Aballéa, Samuel; Mzoughi, Olfa; Toumi, Mondher

    2014-01-01

    Background and objective With constant incentives for healthcare payers to contain their pharmaceutical budgets, modelling policy decision impact became critical. The objective of this project was to test the impact of various policy decisions on pharmaceutical budget (developed for the European Commission for the project ‘European Union (EU) Pharmaceutical expenditure forecast’ – http://ec.europa.eu/health/healthcare/key_documents/index_en.htm). Methods A model was built to assess policy scenarios’ impact on the pharmaceutical budgets of seven member states of the EU, namely France, Germany, Greece, Hungary, Poland, Portugal, and the United Kingdom. The following scenarios were tested: expanding the UK policies to EU, changing time to market access, modifying generic price and penetration, shifting the distribution chain of biosimilars (retail/hospital). Results Applying the UK policy resulted in dramatic savings for Germany (10 times the base case forecast) and substantial additional savings for France and Portugal (2 and 4 times the base case forecast, respectively). Delaying time to market was found be to a very powerful tool to reduce pharmaceutical expenditure. Applying the EU transparency directive (6-month process for pricing and reimbursement) increased pharmaceutical expenditure for all countries (from 1.1 to 4 times the base case forecast), except in Germany (additional savings). Decreasing the price of generics and boosting the penetration rate, as well as shifting distribution of biosimilars through hospital chain were also key methods to reduce pharmaceutical expenditure. Change in the level of reimbursement rate to 100% in all countries led to an important increase in the pharmaceutical budget. Conclusions Forecasting pharmaceutical expenditure is a critical exercise to inform policy decision makers. The most important leverages identified by the model on pharmaceutical budget were driven by generic and biosimilar prices, penetration rate

  15. 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....

  16. Regional Model Nesting Within GFS Daily Forecasts Over West Africa

    Science.gov (United States)

    Druyan, Leonard M.; Fulakeza, Matthew; Lonergan, Patrick; Worrell, Ruben

    2010-01-01

    The study uses the RM3, the regional climate model at the Center for Climate Systems Research of Columbia University and the NASA/Goddard Institute for Space Studies (CCSR/GISS). The paper evaluates 30 48-hour RM3 weather forecasts over West Africa during September 2006 made on a 0.5 grid nested within 1 Global Forecast System (GFS) global forecasts. September 2006 was the Special Observing Period #3 of the African Monsoon Multidisciplinary Analysis (AMMA). Archived GFS initial conditions and lateral boundary conditions for the simulations from the US National Weather Service, National Oceanographic and Atmospheric Administration were interpolated four times daily. Results for precipitation forecasts are validated against Tropical Rainfall Measurement Mission (TRMM) satellite estimates and data from the Famine Early Warning System (FEWS), which includes rain gauge measurements, and forecasts of circulation are compared to reanalysis 2. Performance statistics for the precipitation forecasts include bias, root-mean-square errors and spatial correlation coefficients. The nested regional model forecasts are compared to GFS forecasts to gauge whether nesting provides additional realistic information. They are also compared to RM3 simulations driven by reanalysis 2, representing high potential skill forecasts, to gauge the sensitivity of results to lateral boundary conditions. Nested RM3/GFS forecasts generate excessive moisture advection toward West Africa, which in turn causes prodigious amounts of model precipitation. This problem is corrected by empirical adjustments in the preparation of lateral boundary conditions and initial conditions. The resulting modified simulations improve on the GFS precipitation forecasts, achieving time-space correlations with TRMM of 0.77 on the first day and 0.63 on the second day. One realtime RM3/GFS precipitation forecast made at and posted by the African Centre of Meteorological Application for Development (ACMAD) in Niamey, Niger

  17. A Bayesian joint probability modeling approach for seasonal forecasting of streamflows at multiple sites

    Science.gov (United States)

    Wang, Q. J.; Robertson, D. E.; Chiew, F. H. S.

    2009-05-01

    Seasonal forecasting of streamflows can be highly valuable for water resources management. In this paper, a Bayesian joint probability (BJP) modeling approach for seasonal forecasting of streamflows at multiple sites is presented. A Box-Cox transformed multivariate normal distribution is proposed to model the joint distribution of future streamflows and their predictors such as antecedent streamflows and El Niño-Southern Oscillation indices and other climate indicators. Bayesian inference of model parameters and uncertainties is implemented using Markov chain Monte Carlo sampling, leading to joint probabilistic forecasts of streamflows at multiple sites. The model provides a parametric structure for quantifying relationships between variables, including intersite correlations. The Box-Cox transformed multivariate normal distribution has considerable flexibility for modeling a wide range of predictors and predictands. The Bayesian inference formulated allows the use of data that contain nonconcurrent and missing records. The model flexibility and data-handling ability means that the BJP modeling approach is potentially of wide practical application. The paper also presents a number of statistical measures and graphical methods for verification of probabilistic forecasts of continuous variables. Results for streamflows at three river gauges in the Murrumbidgee River catchment in southeast Australia show that the BJP modeling approach has good forecast quality and that the fitted model is consistent with observed data.

  18. Wind-Farm Forecasting Using the HARMONIE Weather Forecast Model and Bayes Model Averaging for Bias Removal.

    Science.gov (United States)

    O'Brien, Enda; McKinstry, Alastair; Ralph, Adam

    2015-04-01

    Building on previous work presented at EGU 2013 (http://www.sciencedirect.com/science/article/pii/S1876610213016068 ), more results are available now from a different wind-farm in complex terrain in southwest Ireland. The basic approach is to interpolate wind-speed forecasts from an operational weather forecast model (i.e., HARMONIE in the case of Ireland) to the precise location of each wind-turbine, and then use Bayes Model Averaging (BMA; with statistical information collected from a prior training-period of e.g., 25 days) to remove systematic biases. Bias-corrected wind-speed forecasts (and associated power-generation forecasts) are then provided twice daily (at 5am and 5pm) out to 30 hours, with each forecast validation fed back to BMA for future learning. 30-hr forecasts from the operational Met Éireann HARMONIE model at 2.5km resolution have been validated against turbine SCADA observations since Jan. 2014. An extra high-resolution (0.5km grid-spacing) HARMONIE configuration has been run since Nov. 2014 as an extra member of the forecast "ensemble". A new version of HARMONIE with extra filters designed to stabilize high-resolution configurations has been run since Jan. 2015. Measures of forecast skill and forecast errors will be provided, and the contributions made by the various physical and computational enhancements to HARMONIE will be quantified.

  19. Dispersion Modeling Using Ensemble Forecasts Compared to ETEX Measurements.

    Science.gov (United States)

    Straume, Anne Grete; N'dri Koffi, Ernest; Nodop, Katrin

    1998-11-01

    Numerous numerical models are developed to predict long-range transport of hazardous air pollution in connection with accidental releases. When evaluating and improving such a model, it is important to detect uncertainties connected to the meteorological input data. A Lagrangian dispersion model, the Severe Nuclear Accident Program, is used here to investigate the effect of errors in the meteorological input data due to analysis error. An ensemble forecast, produced at the European Centre for Medium-Range Weather Forecasts, is then used as model input. The ensemble forecast members are generated by perturbing the initial meteorological fields of the weather forecast. The perturbations are calculated from singular vectors meant to represent possible forecast developments generated by instabilities in the atmospheric flow during the early part of the forecast. The instabilities are generated by errors in the analyzed fields. Puff predictions from the dispersion model, using ensemble forecast input, are compared, and a large spread in the predicted puff evolutions is found. This shows that the quality of the meteorological input data is important for the success of the dispersion model. In order to evaluate the dispersion model, the calculations are compared with measurements from the European Tracer Experiment. The model manages to predict the measured puff evolution concerning shape and time of arrival to a fairly high extent, up to 60 h after the start of the release. The modeled puff is still too narrow in the advection direction.

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

    Energy Technology Data Exchange (ETDEWEB)

    Lundquist, J; Glascoe, L; Obrecht, J

    2010-03-18

    Accurate short-term forecasts of wind resources are required for efficient wind farm operation and ultimately for the integration of large amounts of wind-generated power into electrical grids. Siemens Energy Inc. and Lawrence Livermore National Laboratory, with the University of Colorado at Boulder, are collaborating on the design of an operational forecasting system for large wind farms. The basis of the system is the numerical weather prediction tool, the Weather Research and Forecasting (WRF) model; large-eddy simulations and data assimilation approaches are used to refine and tailor the forecasting system. Representation of the atmospheric boundary layer is modified, based on high-resolution large-eddy simulations of the atmospheric boundary. These large-eddy simulations incorporate wake effects from upwind turbines on downwind turbines as well as represent complex atmospheric variability due to complex terrain and surface features as well as atmospheric stability. Real-time hub-height wind speed and other meteorological data streams from existing wind farms are incorporated into the modeling system to enable uncertainty quantification through probabilistic forecasts. A companion investigation has identified optimal boundary-layer physics options for low-level forecasts in complex terrain, toward employing decadal WRF simulations to anticipate large-scale changes in wind resource availability due to global climate change.

  1. Hybrid Stochastic Forecasting Model for Management of Large Open Water Reservoir with Storage Function

    Science.gov (United States)

    Kozel, Tomas; Stary, Milos

    2017-12-01

    The main advantage of stochastic forecasting is fan of possible value whose deterministic method of forecasting could not give us. Future development of random process is described better by stochastic then deterministic forecasting. Discharge in measurement profile could be categorized as random process. Content of article is construction and application of forecasting model for managed large open water reservoir with supply function. Model is based on neural networks (NS) and zone models, which forecasting values of average monthly flow from inputs values of average monthly flow, learned neural network and random numbers. Part of data was sorted to one moving zone. The zone is created around last measurement average monthly flow. Matrix of correlation was assembled only from data belonging to zone. The model was compiled for forecast of 1 to 12 month with using backward month flows (NS inputs) from 2 to 11 months for model construction. Data was got ridded of asymmetry with help of Box-Cox rule (Box, Cox, 1964), value r was found by optimization. In next step were data transform to standard normal distribution. The data were with monthly step and forecast is not recurring. 90 years long real flow series was used for compile of the model. First 75 years were used for calibration of model (matrix input-output relationship), last 15 years were used only for validation. Outputs of model were compared with real flow series. For comparison between real flow series (100% successfully of forecast) and forecasts, was used application to management of artificially made reservoir. Course of water reservoir management using Genetic algorithm (GE) + real flow series was compared with Fuzzy model (Fuzzy) + forecast made by Moving zone model. During evaluation process was founding the best size of zone. Results show that the highest number of input did not give the best results and ideal size of zone is in interval from 25 to 35, when course of management was almost same for

  2. Short-term forecasting model for aggregated regional hydropower generation

    International Nuclear Information System (INIS)

    Monteiro, Claudio; Ramirez-Rosado, Ignacio J.; Fernandez-Jimenez, L. Alfredo

    2014-01-01

    Highlights: • Original short-term forecasting model for the hourly hydropower generation. • The use of NWP forecasts allows horizons of several days. • New variable to represent the capacity level for generating hydroelectric energy. • The proposed model significantly outperforms the persistence model. - Abstract: This paper presents an original short-term forecasting model of the hourly electric power production for aggregated regional hydropower generation. The inputs of the model are previously recorded values of the aggregated hourly production of hydropower plants and hourly water precipitation forecasts using Numerical Weather Prediction tools, as well as other hourly data (load demand and wind generation). This model is composed of three modules: the first one gives the prediction of the “monthly” hourly power production of the hydropower plants; the second module gives the prediction of hourly power deviation values, which are added to that obtained by the first module to achieve the final forecast of the hourly hydropower generation; the third module allows a periodic adjustment of the prediction of the first module to improve its BIAS error. The model has been applied successfully to the real-life case study of the short-term forecasting of the aggregated hydropower generation in Spain and Portugal (Iberian Peninsula Power System), achieving satisfactory results for the next-day forecasts. The model can be valuable for agents involved in electricity markets and useful for power system operations

  3. With string model to time series forecasting

    Science.gov (United States)

    Pinčák, Richard; Bartoš, Erik

    2015-10-01

    Overwhelming majority of econometric models applied on a long term basis in the financial forex market do not work sufficiently well. The reason is that transaction costs and arbitrage opportunity are not included, as this does not simulate the real financial markets. Analyses are not conducted on the non equidistant date but rather on the aggregate date, which is also not a real financial case. In this paper, we would like to show a new way how to analyze and, moreover, forecast financial market. We utilize the projections of the real exchange rate dynamics onto the string-like topology in the OANDA market. The latter approach allows us to build the stable prediction models in trading in the financial forex market. The real application of the multi-string structures is provided to demonstrate our ideas for the solution of the problem of the robust portfolio selection. The comparison with the trend following strategies was performed, the stability of the algorithm on the transaction costs for long trade periods was confirmed.

  4. Morehead City, North Carolina Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Morehead City, North Carolina Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  5. Christiansted, Virgin Islands Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Christiansted, Virgin Islands Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  6. Arena Cove, California Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Arena Cove, California Forecast Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  7. Atlantic City, New Jersey Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Atlantic City, New Jersey Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  8. Crescent City, California Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Crescent City, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  9. Charlotte Amalie, Virgin Islands Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Charlotte Amalie, Virgin Islands Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami...

  10. Pago Pago, American Samoa Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Pago Pago, American Samoa Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  11. Statistical Studies of Mesoscale Forecast Models MM5 and WRF

    National Research Council Canada - National Science Library

    Henmi, Teizi

    2004-01-01

    ... models were carried out and the results were compared with surface observation data. Both models tended to overforecast temperature and dew-point temperature, although the correlation coefficients between forecast and observations were fairly high...

  12. Myrtle Beach, South Carolina Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Myrtle Beach, South Carolina Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  13. Los Angeles, California Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Los Angeles, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  14. Port Angeles, Washington Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Port Angeles, Washington Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  15. Virginia Beach Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Virginia Beach, Virginia Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  16. Cordova, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Cordova, Alaska Forecast Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...

  17. Nantucket, Massachusetts Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Nantucket, Massachusetts Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  18. Unalaska, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Unalaska, Alaska Forecast Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...

  19. Santa Barbara, California Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Santa Barbara, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  20. Point Reyes, California Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Point Reyes, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  1. Port San Luis, California Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Port San Luis, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  2. Eureka, California Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Eureka, California Forecast Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...

  3. Cape Hatteras, North Carolina Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Cape Hatteras, North Carolina Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  4. San Francisco, California Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The San Francisco, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  5. Elfin Cove, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Elfin Cove, Alaska Forecast Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...

  6. British Columbia, Canada Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The British Columbia, Canada Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  7. Chignik, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Chignik, Alaska Forecast Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...

  8. Cash flow forecasting model for nuclear power projects

    International Nuclear Information System (INIS)

    Liu Wei; Guo Jilin

    2002-01-01

    Cash flow forecasting is very important for owners and contractors of nuclear power projects to arrange the capital and to decrease the capital cost. The factors related to contractor cash flow forecasting are analyzed and a cash flow forecasting model is presented which is suitable for both contractors and owners. The model is efficiently solved using a cost-schedule data integration scheme described. A program is developed based on the model and verified with real project data. The result indicates that the model is efficient and effective

  9. Short-Term Distribution System State Forecast Based on Optimal Synchrophasor Sensor Placement and Extreme Learning Machine

    Energy Technology Data Exchange (ETDEWEB)

    Jiang, Huaiguang; Zhang, Yingchen

    2016-11-14

    This paper proposes an approach for distribution system state forecasting, which aims to provide an accurate and high speed state forecasting with an optimal synchrophasor sensor placement (OSSP) based state estimator and an extreme learning machine (ELM) based forecaster. Specifically, considering the sensor installation cost and measurement error, an OSSP algorithm is proposed to reduce the number of synchrophasor sensor and keep the whole distribution system numerically and topologically observable. Then, the weighted least square (WLS) based system state estimator is used to produce the training data for the proposed forecaster. Traditionally, the artificial neural network (ANN) and support vector regression (SVR) are widely used in forecasting due to their nonlinear modeling capabilities. However, the ANN contains heavy computation load and the best parameters for SVR are difficult to obtain. In this paper, the ELM, which overcomes these drawbacks, is used to forecast the future system states with the historical system states. The proposed approach is effective and accurate based on the testing results.

  10. Introducing uncertainty of radar-rainfall estimates to the verification of mesoscale model precipitation forecasts

    Directory of Open Access Journals (Sweden)

    M. P. Mittermaier

    2008-05-01

    Full Text Available A simple measure of the uncertainty associated with using radar-derived rainfall estimates as "truth" has been introduced to the Numerical Weather Prediction (NWP verification process to assess the effect on forecast skill and errors. Deterministic precipitation forecasts from the mesoscale version of the UK Met Office Unified Model for a two-day high-impact event and for a month were verified at the daily and six-hourly time scale using a spatially-based intensity-scale method and various traditional skill scores such as the Equitable Threat Score (ETS and log-odds ratio. Radar-rainfall accumulations from the UK Nimrod radar-composite were used.

    The results show that the inclusion of uncertainty has some effect, shifting the forecast errors and skill. The study also allowed for the comparison of results from the intensity-scale method and traditional skill scores. It showed that the two methods complement each other, one detailing the scale and rainfall accumulation thresholds where the errors occur, the other showing how skillful the forecast is. It was also found that for the six-hourly forecasts the error distributions remain similar with forecast lead time but skill decreases. This highlights the difference between forecast error and forecast skill, and that they are not necessarily the same.

  11. Estimating the Value of Improved Distributed Photovoltaic Adoption Forecasts for Utility Resource Planning

    Energy Technology Data Exchange (ETDEWEB)

    Gagnon, Pieter [National Renewable Energy Lab. (NREL), Golden, CO (United States); Barbose, Galen L. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Stoll, Brady [National Renewable Energy Lab. (NREL), Golden, CO (United States); Ehlen, Ali [National Renewable Energy Lab. (NREL), Golden, CO (United States); Zuboy, Jarret [National Renewable Energy Lab. (NREL), Golden, CO (United States); Mai, Trieu [National Renewable Energy Lab. (NREL), Golden, CO (United States); Mills, Andrew D. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

    2018-05-15

    Misforecasting the adoption of customer-owned distributed photovoltaics (DPV) can have operational and financial implications for utilities; forecasting capabilities can be improved, but generally at a cost. This paper informs this decision-space by using a suite of models to explore the capacity expansion and operation of the Western Interconnection over a 15-year period across a wide range of DPV growth rates and misforecast severities. The system costs under a misforecast are compared against the costs under a perfect forecast, to quantify the costs of misforecasting. Using a simplified probabilistic method applied to these modeling results, an analyst can make a first-order estimate of the financial benefit of improving a utility’s forecasting capabilities, and thus be better informed about whether to make such an investment. For example, under our base assumptions, a utility with 10 TWh per year of retail electric sales who initially estimates that DPV growth could range from 2% to 7.5% of total generation over the next 15 years could expect total present-value savings of approximately $4 million if they could reduce the severity of misforecasting to within ±25%. Utility resource planners can compare those savings against the costs needed to achieve that level of precision, to guide their decision on whether to make an investment in tools or resources.

  12. Forecasting electric demand of distribution system planing in rural and sparsely populated regions

    Energy Technology Data Exchange (ETDEWEB)

    Willis, H.L.; Buri, M.J. [ABB Automated Distribution Div., Raleigh, NC (United States); Finley, L.A. [Snohomish County PUD, Everett, WA (United States)

    1995-11-01

    Modern computerized distribution load forecasting methods, although accurate when applied to urban areas, give somewhat less satisfactory results when forecasting load growth in sparsely populated rural areas. This paper examines the differences between rural and urban load growth histories, identifying a major difference in the observed behavior of load growth. This difference is exploited in a new simulation forecasting algorithm. Tests show the new method is as accurate in forecasting rural load growth and as useful for analyzing DSM impacts than past methods, while requiring considerably lower computer resources and data than other simulation methods of comparable accuracy.

  13. Forecasting Analysis of Shanghai Stock Index Based on ARIMA Model

    Directory of Open Access Journals (Sweden)

    Li Chenggang

    2017-01-01

    Full Text Available Prediction and analysis of the Shanghai Composite Index is conducive for investors to investing in the stock market, and providing investors with reference. This paper selects Shanghai Composite Index monthly closing price from Jan, 2005 to Oct, 2016 to construct ARIMA model. This paper carries on the forecast of the last three monthly closing price of Shanghai Stock Index that have occurred, and compared it with the actual value, which tests the accuracy and feasibility of the model in the short term Shanghai Stock Index forecast. At last, this paper uses the ARIMA model to forecast the Shanghai Composite Index closing price of the last two months in 2016.

  14. The Impact of Distributed Generation Systems in the Load Forecasting

    OpenAIRE

    Benedicto Llorens, Juan Manuel

    2009-01-01

    Projecte fet en col.laboració amb l'Instituto Superior Tecnico. Universidade Técnica de Lisboa Load forecasting is vitally important for the electric industry in the deregulated economy. It has many applications including energy purchasing and generation, load switching, contract evaluation and infrastructure development. Because of this, a large variety of mathematical methods have been developed for load forecasting. In addition, the large-scale integration of wind power, now...

  15. Verification of short lead time forecast models: applied to Kp and Dst forecasting

    Science.gov (United States)

    Wintoft, Peter; Wik, Magnus

    2016-04-01

    In the ongoing EU/H2020 project PROGRESS models that predicts Kp, Dst, and AE from L1 solar wind data will be used as inputs to radiation belt models. The possible lead times from L1 measurements are shorter (10s of minutes to hours) than the typical duration of the physical phenomena that should be forecast. Under these circumstances several metrics fail to single out trivial cases, such as persistence. In this work we explore metrics and approaches for short lead time forecasts. We apply these to current Kp and Dst forecast models. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 637302.

  16. Very short-term probabilistic forecasting of wind power with generalized logit-Normal distributions

    DEFF Research Database (Denmark)

    Pinson, Pierre

    2012-01-01

    and probability masses at the bounds. Both auto-regressive and conditional parametric auto-regressive models are considered for the dynamics of their location and scale parameters. Estimation is performed in a recursive least squares framework with exponential forgetting. The superiority of this proposal over......Very-short-term probabilistic forecasts, which are essential for an optimal management of wind generation, ought to account for the non-linear and double-bounded nature of that stochastic process. They take here the form of discrete–continuous mixtures of generalized logit–normal distributions...

  17. A hybrid spatiotemporal drought forecasting model for operational use

    Science.gov (United States)

    Vasiliades, L.; Loukas, A.

    2010-09-01

    Drought forecasting plays an important role in the planning and management of natural resources and water resource systems in a river basin. Early and timelines forecasting of a drought event can help to take proactive measures and set out drought mitigation strategies to alleviate the impacts of drought. Spatiotemporal data mining is the extraction of unknown and implicit knowledge, structures, spatiotemporal relationships, or patterns not explicitly stored in spatiotemporal databases. As one of data mining techniques, forecasting is widely used to predict the unknown future based upon the patterns hidden in the current and past data. This study develops a hybrid spatiotemporal scheme for integrated spatial and temporal forecasting. Temporal forecasting is achieved using feed-forward neural networks and the temporal forecasts are extended to the spatial dimension using a spatial recurrent neural network model. The methodology is demonstrated for an operational meteorological drought index the Standardized Precipitation Index (SPI) calculated at multiple timescales. 48 precipitation stations and 18 independent precipitation stations, located at Pinios river basin in Thessaly region, Greece, were used for the development and spatiotemporal validation of the hybrid spatiotemporal scheme. Several quantitative temporal and spatial statistical indices were considered for the performance evaluation of the models. Furthermore, qualitative statistical criteria based on contingency tables between observed and forecasted drought episodes were calculated. The results show that the lead time of forecasting for operational use depends on the SPI timescale. The hybrid spatiotemporal drought forecasting model could be operationally used for forecasting up to three months ahead for SPI short timescales (e.g. 3-6 months) up to six months ahead for large SPI timescales (e.g. 24 months). The above findings could be useful in developing a drought preparedness plan in the region.

  18. 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)

  19. 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)

  20. A hierarchical spatiotemporal analog forecasting model for count data.

    Science.gov (United States)

    McDermott, Patrick L; Wikle, Christopher K; Millspaugh, Joshua

    2018-01-01

    Analog forecasting is a mechanism-free nonlinear method that forecasts a system forward in time by examining how past states deemed similar to the current state moved forward. Previous applications of analog forecasting has been successful at producing robust forecasts for a variety of ecological and physical processes, but it has typically been presented in an empirical or heuristic procedure, rather than as a formal statistical model. The methodology presented here extends the model-based analog method of McDermott and Wikle (Environmetrics, 27, 2016, 70) by placing analog forecasting within a fully hierarchical statistical framework that can accommodate count observations. Using a Bayesian approach, the hierarchical analog model is able to quantify rigorously the uncertainty associated with forecasts. Forecasting waterfowl settling patterns in the northwestern United States and Canada is conducted by applying the hierarchical analog model to a breeding population survey dataset. Sea surface temperature (SST) in the Pacific Ocean is used to help identify potential analogs for the waterfowl settling patterns.

  1. Mean Bias in Seasonal Forecast Model and ENSO Prediction Error.

    Science.gov (United States)

    Kim, Seon Tae; Jeong, Hye-In; Jin, Fei-Fei

    2017-07-20

    This study uses retrospective forecasts made using an APEC Climate Center seasonal forecast model to investigate the cause of errors in predicting the amplitude of El Niño Southern Oscillation (ENSO)-driven sea surface temperature variability. When utilizing Bjerknes coupled stability (BJ) index analysis, enhanced errors in ENSO amplitude with forecast lead times are found to be well represented by those in the growth rate estimated by the BJ index. ENSO amplitude forecast errors are most strongly associated with the errors in both the thermocline slope response and surface wind response to forcing over the tropical Pacific, leading to errors in thermocline feedback. This study concludes that upper ocean temperature bias in the equatorial Pacific, which becomes more intense with increasing lead times, is a possible cause of forecast errors in the thermocline feedback and thus in ENSO amplitude.

  2. Evaluation of induced seismicity forecast models in the Induced Seismicity Test Bench

    Science.gov (United States)

    Király, Eszter; Gischig, Valentin; Zechar, Jeremy; Doetsch, Joseph; Karvounis, Dimitrios; Wiemer, Stefan

    2016-04-01

    Induced earthquakes often accompany fluid injection, and the seismic hazard they pose threatens various underground engineering projects. Models to monitor and control induced seismic hazard with traffic light systems should be probabilistic, forward-looking, and updated as new data arrive. Here, we propose an Induced Seismicity Test Bench to test and rank such models. We apply the test bench to data from the Basel 2006 and Soultz-sous-Forêts 2004 geothermal stimulation projects, and we assess forecasts from two models that incorporate a different mix of physical understanding and stochastic representation of the induced sequences: Shapiro in Space (SiS) and Hydraulics and Seismics (HySei). SiS is based on three pillars: the seismicity rate is computed with help of the seismogenic index and a simple exponential decay of the seismicity; the magnitude distribution follows the Gutenberg-Richter relation; and seismicity is distributed in space based on smoothing seismicity during the learning period with 3D Gaussian kernels. The HySei model describes seismicity triggered by pressure diffusion with irreversible permeability enhancement. Our results show that neither model is fully superior to the other. HySei forecasts the seismicity rate well, but is only mediocre at forecasting the spatial distribution. On the other hand, SiS forecasts the spatial distribution well but not the seismicity rate. The shut-in phase is a difficult moment for both models in both reservoirs: the models tend to underpredict the seismicity rate around, and shortly after, shut-in. Ensemble models that combine HySei's rate forecast with SiS's spatial forecast outperform each individual model.

  3. Forecasting the Rupture Directivity of Large Earthquakes: Centroid Bias of the Conditional Hypocenter Distribution

    Science.gov (United States)

    Donovan, J.; Jordan, T. H.

    2012-12-01

    Forecasting the rupture directivity of large earthquakes is an important problem in probabilistic seismic hazard analysis (PSHA), because directivity is known to strongly influence ground motions. We describe how rupture directivity can be forecast in terms of the "conditional hypocenter distribution" or CHD, defined to be the probability distribution of a hypocenter given the spatial distribution of moment release (fault slip). The simplest CHD is a uniform distribution, in which the hypocenter probability density equals the moment-release probability density. For rupture models in which the rupture velocity and rise time depend only on the local slip, the CHD completely specifies the distribution of the directivity parameter D, defined in terms of the degree-two polynomial moments of the source space-time function. This parameter, which is zero for a bilateral rupture and unity for a unilateral rupture, can be estimated from finite-source models or by the direct inversion of seismograms (McGuire et al., 2002). We compile D-values from published studies of 65 large earthquakes and show that these data are statistically inconsistent with the uniform CHD advocated by McGuire et al. (2002). Instead, the data indicate a "centroid biased" CHD, in which the expected distance between the hypocenter and the hypocentroid is less than that of a uniform CHD. In other words, the observed directivities appear to be closer to bilateral than predicted by this simple model. We discuss the implications of these results for rupture dynamics and fault-zone heterogeneities. We also explore their PSHA implications by modifying the CyberShake simulation-based hazard model for the Los Angeles region, which assumed a uniform CHD (Graves et al., 2011).

  4. A forecasting model of gaming revenues in Clark County, Nevada

    International Nuclear Information System (INIS)

    Edwards, B.; Bando, A.; Basset, G.; Rosen, A.; Meenan, C.; Carlson, J.

    1992-01-01

    This paper describes the Western Area Gaming and Economic Response Simulator (WAGERS), a forecasting model that emphasizes the role of the gaming industry in Clark County, Nevada. It is designed to generate forecasts of gaming revenues in Clark County, whose regional economy is dominated by the gaming industry, and identify the exogenous variables that affect gaming revenues. This model will provide baseline forecasts of Clark County gaming revenues in order to assess changes in gaming related economic activity resulting from future events like the siting of a permanent high-level radioactive waste repository at Yucca Mountain

  5. Forecasting daily meteorological time series using ARIMA and regression models

    Science.gov (United States)

    Murat, Małgorzata; Malinowska, Iwona; Gos, Magdalena; Krzyszczak, Jaromir

    2018-04-01

    The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt- Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.

  6. A forecasting model of gaming revenues in Clark County, Nevada

    International Nuclear Information System (INIS)

    Edwards, B.; Bando, A.; Bassett, G.; Rosen, A.; Carlson, J.; Meenan, C.

    1992-01-01

    This paper describes the Western Area Gaining and Economic Response Simulator (WAGERS), a forecasting model that emphasizes the role of the gaming industry in Clark County, Nevada. It is designed to generate forecasts of gaming revenues in Clark County, whose regional economy is dominated by the gaming industry, an identify the exogenous variables that affect gaming revenues. This model will provide baseline forecasts of Clark County gaming revenues in order to assess changes in gaming related economic activity resulting from future events like the siting of a permanent high-level radioactive waste repository at Yucca Mountain

  7. A forecasting model of gaming revenues in Clark County, Nevada

    Energy Technology Data Exchange (ETDEWEB)

    Edwards, B.; Bando, A.; Bassett, G.; Rosen, A. [Argonne National Lab., IL (United States); Carlson, J.; Meenan, C. [Science Applications International Corp., Las Vegas, NV (United States)

    1992-04-01

    This paper describes the Western Area Gaming and Economic Response Simulator (WAGERS), a forecasting model that emphasizes the role of the gaming industry in Clark County, Nevada. It is designed to generate forecasts of gaming revenues in Clark County, whose regional economy is dominated by the gaming industry, an identify the exogenous variables that affect gaming revenues. This model will provide baseline forecasts of Clark County gaming revenues in order to assess changes in gaming related economic activity resulting from future events like the siting of a permanent high-level radioactive waste repository at Yucca Mountain.

  8. A dynamic bivariate Poisson model for analysing and forecasting match results in the English Premier League

    NARCIS (Netherlands)

    Koopman, S.J.; Lit, R.

    2015-01-01

    Summary: We develop a statistical model for the analysis and forecasting of football match results which assumes a bivariate Poisson distribution with intensity coefficients that change stochastically over time. The dynamic model is a novelty in the statistical time series analysis of match results

  9. Human-model hybrid Korean air quality forecasting system.

    Science.gov (United States)

    Chang, Lim-Seok; Cho, Ara; Park, Hyunju; Nam, Kipyo; Kim, Deokrae; Hong, Ji-Hyoung; Song, Chang-Keun

    2016-09-01

    The Korean national air quality forecasting system, consisting of the Weather Research and Forecasting, the Sparse Matrix Operator Kernel Emissions, and the Community Modeling and Analysis (CMAQ), commenced from August 31, 2013 with target pollutants of particulate matters (PM) and ozone. Factors contributing to PM forecasting accuracy include CMAQ inputs of meteorological field and emissions, forecasters' capacity, and inherent CMAQ limit. Four numerical experiments were conducted including two global meteorological inputs from the Global Forecast System (GFS) and the Unified Model (UM), two emissions from the Model Intercomparison Study Asia (MICS-Asia) and the Intercontinental Chemical Transport Experiment (INTEX-B) for the Northeast Asia with Clear Air Policy Support System (CAPSS) for South Korea, and data assimilation of the Monitoring Atmospheric Composition and Climate (MACC). Significant PM underpredictions by using both emissions were found for PM mass and major components (sulfate and organic carbon). CMAQ predicts PM2.5 much better than PM10 (NMB of PM2.5: -20~-25%, PM10: -43~-47%). Forecasters' error usually occurred at the next day of high PM event. Once CMAQ fails to predict high PM event the day before, forecasters are likely to dismiss the model predictions on the next day which turns out to be true. The best combination of CMAQ inputs is the set of UM global meteorological field, MICS-Asia and CAPSS 2010 emissions with the NMB of -12.3%, the RMSE of 16.6μ/m(3) and the R(2) of 0.68. By using MACC data as an initial and boundary condition, the performance skill of CMAQ would be improved, especially in the case of undefined coarse emission. A variety of methods such as ensemble and data assimilation are considered to improve further the accuracy of air quality forecasting, especially for high PM events to be comparable to for all cases. The growing utilization of the air quality forecast induced the public strongly to demand that the accuracy of the

  10. Medium Range Forecast (MRF) and Nested Grid Model (NGM)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Nested Grid Model (NGM) and Medium Range Forecast (MRF) Archive is historical digital data set DSI-6140, archived at the NOAA National Centers for Environmental...

  11. Empirical evaluation of a forecasting model for successful facilitation ...

    African Journals Online (AJOL)

    During 2000 the annual Facilitator Customer Satisfaction Survey was ... the forecasting model is successful concerning the CSI value and a high positive linear ... namely that of human behaviour to incorporate other influences than just the ...

  12. forecasting with nonlinear time series model: a monte-carlo

    African Journals Online (AJOL)

    PUBLICATIONS1

    Carlo method of forecasting using a special nonlinear time series model, called logistic smooth transition ... We illustrate this new method using some simulation ..... in MATLAB 7.5.0. ... process (DGP) using the logistic smooth transi-.

  13. Development of rainfall-runoff forecast model | Oyebode | Journal of ...

    African Journals Online (AJOL)

    ... and meterological variables involved in rainfall-runoff process to improve forecast accuracy of rainfallrunoff. ... The simulation was done using MATLAB® 7.0. The simulation results showed that neurofuzzy-based model has higher coefficient ...

  14. Evaluation and Application of the Weather Research and Forecast Model

    National Research Council Canada - National Science Library

    Passner, Jeffrey E

    2007-01-01

    ... by the U.S. Army Research Laboratory (ARL) to determine how accurate and robust the model is under a variety of meteorological conditions, with an emphasis on fine resolution, short-range forecasts in complex terrain...

  15. Formation of an Integrated Stock Price Forecast Model in Lithuania

    Directory of Open Access Journals (Sweden)

    Audrius Dzikevičius

    2016-12-01

    Full Text Available Technical and fundamental analyses are widely used to forecast stock prices due to lack of knowledge of other modern models and methods such as Residual Income Model, ANN-APGARCH, Support Vector Machine, Probabilistic Neural Network and Genetic Fuzzy Systems. Although stock price forecast models integrating both technical and fundamental analyses are currently used widely, their integration is not justified comprehensively enough. This paper discusses theoretical one-factor and multi-factor stock price forecast models already applied by investors at a global level and determines possibility to create and apply practically a stock price forecast model which integrates fundamental and technical analysis with the reference to the Lithuanian stock market. The research is aimed to determine the relationship between stock prices of the 14 Lithuanian companies listed in the Main List by the Nasdaq OMX Baltic and various fundamental variables. Based on correlation and regression analysis results and application of c-Squared Test, ANOVA method, a general stock price forecast model is generated. This paper discusses practical implications how the developed model can be used to forecast stock prices by individual investors and suggests additional check measures.

  16. Modelling and forecasting Turkish residential electricity demand

    International Nuclear Information System (INIS)

    Dilaver, Zafer; Hunt, Lester C

    2011-01-01

    This research investigates the relationship between Turkish residential electricity consumption, household total final consumption expenditure and residential electricity prices by applying the structural time series model to annual data over the period from 1960 to 2008. Household total final consumption expenditure, real energy prices and an underlying energy demand trend are found to be important drivers of Turkish residential electricity demand with the estimated short run and the long run total final consumption expenditure elasticities being 0.38 and 1.57, respectively, and the estimated short run and long run price elasticities being -0.09 and -0.38, respectively. Moreover, the estimated underlying energy demand trend, (which, as far as is known, has not been investigated before for the Turkish residential sector) should be of some benefit to Turkish decision makers in terms of energy planning. It provides information about the impact of past policies, the influence of technical progress, the impacts of changes in consumer behaviour and the effects of changes in economic structure. Furthermore, based on the estimated equation, and different forecast assumptions, it is predicted that Turkish residential electricity demand will be somewhere between 48 and 80 TWh by 2020 compared to 40 TWh in 2008. - Research highlights: → Estimated short run and long run expenditure elasticities of 0.38 and 1.57, respectively. → Estimated short run and long run price elasticities of -0.09 and -0.38, respectively. → Estimated UEDT has increasing (i.e. energy using) and decreasing (i.e. energy saving) periods. → Predicted Turkish residential electricity demand between 48 and 80 TWh in 2020.

  17. Drought Forecasting with Vegetation Temperature Condition Index Using ARIMA Models in the Guanzhong Plain

    Directory of Open Access Journals (Sweden)

    Miao Tian

    2016-08-01

    Full Text Available This paper works on the agricultural drought forecasting in the Guanzhong Plain of China using Autoregressive Integrated Moving Average (ARIMA models based on the time series of drought monitoring results of Vegetation Temperature Condition Index (VTCI. About 90 VTCI images derived from Advanced Very High Resolution Radiometer (AVHRR data were selected to develop the ARIMA models from the erecting stage to the maturity stage of winter wheat (early March to late May in each year at a ten-day interval of the years from 2000 to 2009. We take the study area overlying on the administration map around the study area, and divide the study area into 17 parts where at least one weather station is located in each part. The pixels where the 17 weather stations are located are firstly chosen and studied for their fitting models, and then the best models for all pixels of the whole area are determined. According to the procedures for the models’ development, the selected best models for the 17 pixels are identified and the forecast is done with three steps. The forecasting results of the ARIMA models were compared with the monitoring ones. The results show that with reference to the categorized VTCI drought monitoring results, the categorized forecasting results of the ARIMA models are in good agreement with the monitoring ones. The categorized drought forecasting results of the ARIMA models are more severity in the northeast of the Plain in April 2009, which are in good agreements with the monitoring ones. The absolute errors of the AR(1 models are lower than the SARIMA models, both in the frequency distributions and in the statistic results. However, the ability of SARIMA models to detect the changes of the drought situation is better than the AR(1 models. These results indicate that the ARIMA models can better forecast the category and extent of droughts and can be applied to forecast droughts in the Plain.

  18. Post-processing of multi-model ensemble river discharge forecasts using censored EMOS

    Science.gov (United States)

    Hemri, Stephan; Lisniak, Dmytro; Klein, Bastian

    2014-05-01

    When forecasting water levels and river discharge, ensemble weather forecasts are used as meteorological input to hydrologic process models. As hydrologic models are imperfect and the input ensembles tend to be biased and underdispersed, the output ensemble forecasts for river runoff typically are biased and underdispersed, too. Thus, statistical post-processing is required in order to achieve calibrated and sharp predictions. Standard post-processing methods such as Ensemble Model Output Statistics (EMOS) that have their origins in meteorological forecasting are now increasingly being used in hydrologic applications. Here we consider two sub-catchments of River Rhine, for which the forecasting system of the Federal Institute of Hydrology (BfG) uses runoff data that are censored below predefined thresholds. To address this methodological challenge, we develop a censored EMOS method that is tailored to such data. The censored EMOS forecast distribution can be understood as a mixture of a point mass at the censoring threshold and a continuous part based on a truncated normal distribution. Parameter estimates of the censored EMOS model are obtained by minimizing the Continuous Ranked Probability Score (CRPS) over the training dataset. Model fitting on Box-Cox transformed data allows us to take account of the positive skewness of river discharge distributions. In order to achieve realistic forecast scenarios over an entire range of lead-times, there is a need for multivariate extensions. To this end, we smooth the marginal parameter estimates over lead-times. In order to obtain realistic scenarios of discharge evolution over time, the marginal distributions have to be linked with each other. To this end, the multivariate dependence structure can either be adopted from the raw ensemble like in Ensemble Copula Coupling (ECC), or be estimated from observations in a training period. The censored EMOS model has been applied to multi-model ensemble forecasts issued on a

  19. Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting

    Directory of Open Access Journals (Sweden)

    Yuqi Dong

    2016-12-01

    Full Text Available Accurate short-term electrical load forecasting plays a pivotal role in the national economy and people’s livelihood through providing effective future plans and ensuring a reliable supply of sustainable electricity. Although considerable work has been done to select suitable models and optimize the model parameters to forecast the short-term electrical load, few models are built based on the characteristics of time series, which will have a great impact on the forecasting accuracy. For that reason, this paper proposes a hybrid model based on data decomposition considering periodicity, trend and randomness of the original electrical load time series data. Through preprocessing and analyzing the original time series, the generalized regression neural network optimized by genetic algorithm is used to forecast the short-term electrical load. The experimental results demonstrate that the proposed hybrid model can not only achieve a good fitting ability, but it can also approximate the actual values when dealing with non-linear time series data with periodicity, trend and randomness.

  20. Spatial Distribution of the Coefficient of Variation and Bayesian Forecast for the Paleo-Earthquakes in Japan

    Science.gov (United States)

    Nomura, Shunichi; Ogata, Yosihiko

    2016-04-01

    We propose a Bayesian method of probability forecasting for recurrent earthquakes of inland active faults in Japan. Renewal processes with the Brownian Passage Time (BPT) distribution are applied for over a half of active faults in Japan by the Headquarters for Earthquake Research Promotion (HERP) of Japan. Long-term forecast with the BPT distribution needs two parameters; the mean and coefficient of variation (COV) for recurrence intervals. The HERP applies a common COV parameter for all of these faults because most of them have very few specified paleoseismic events, which is not enough to estimate reliable COV values for respective faults. However, different COV estimates are proposed for the same paleoseismic catalog by some related works. It can make critical difference in forecast to apply different COV estimates and so COV should be carefully selected for individual faults. Recurrence intervals on a fault are, on the average, determined by the long-term slip rate caused by the tectonic motion but fluctuated by nearby seismicities which influence surrounding stress field. The COVs of recurrence intervals depend on such stress perturbation and so have spatial trends due to the heterogeneity of tectonic motion and seismicity. Thus we introduce a spatial structure on its COV parameter by Bayesian modeling with a Gaussian process prior. The COVs on active faults are correlated and take similar values for closely located faults. It is found that the spatial trends in the estimated COV values coincide with the density of active faults in Japan. We also show Bayesian forecasts by the proposed model using Markov chain Monte Carlo method. Our forecasts are different from HERP's forecast especially on the active faults where HERP's forecasts are very high or low.

  1. Macroeconomic Forecasts in Models with Bayesian Averaging of Classical Estimates

    Directory of Open Access Journals (Sweden)

    Piotr Białowolski

    2012-03-01

    Full Text Available The aim of this paper is to construct a forecasting model oriented on predicting basic macroeconomic variables, namely: the GDP growth rate, the unemployment rate, and the consumer price inflation. In order to select the set of the best regressors, Bayesian Averaging of Classical Estimators (BACE is employed. The models are atheoretical (i.e. they do not reflect causal relationships postulated by the macroeconomic theory and the role of regressors is played by business and consumer tendency survey-based indicators. Additionally, survey-based indicators are included with a lag that enables to forecast the variables of interest (GDP, unemployment, and inflation for the four forthcoming quarters without the need to make any additional assumptions concerning the values of predictor variables in the forecast period.  Bayesian Averaging of Classical Estimators is a method allowing for full and controlled overview of all econometric models which can be obtained out of a particular set of regressors. In this paper authors describe the method of generating a family of econometric models and the procedure for selection of a final forecasting model. Verification of the procedure is performed by means of out-of-sample forecasts of main economic variables for the quarters of 2011. The accuracy of the forecasts implies that there is still a need to search for new solutions in the atheoretical modelling.

  2. Coastal and Riverine Flood Forecast Model powered by ADCIRC

    Science.gov (United States)

    Khalid, A.; Ferreira, C.

    2017-12-01

    Coastal flooding is becoming a major threat to increased population in the coastal areas. To protect coastal communities from tropical storms & hurricane damages, early warning systems are being developed. These systems have the capability of real time flood forecasting to identify hazardous coastal areas and aid coastal communities in rescue operations. State of the art hydrodynamic models forced by atmospheric forcing have given modelers the ability to forecast storm surge, water levels and currents. This helps to identify the areas threatened by intense storms. Study on Chesapeake Bay area has gained national importance because of its combined riverine and coastal phenomenon, which leads to greater uncertainty in flood predictions. This study presents an automated flood forecast system developed by following Advanced Circulation (ADCIRC) Surge Guidance System (ASGS) guidelines and tailored to take in riverine and coastal boundary forcing, thus includes all the hydrodynamic processes to forecast total water in the Potomac River. As studies on tidal and riverine flow interaction are very scarce in number, our forecast system would be a scientific tool to examine such area and fill the gaps with precise prediction for Potomac River. Real-time observations from National Oceanic and Atmospheric Administration (NOAA) and field measurements have been used as model boundary feeding. The model performance has been validated by using major historical riverine and coastal flooding events. Hydrodynamic model ADCIRC produced promising predictions for flood inundation areas. As better forecasts can be achieved by using coupled models, this system is developed to take boundary conditions from Global WaveWatchIII for the research purposes. Wave and swell propagation will be fed through Global WavewatchIII model to take into account the effects of swells and currents. This automated forecast system is currently undergoing rigorous testing to include any missing parameters which

  3. Statistical bias correction modelling for seasonal rainfall forecast for the case of Bali island

    Science.gov (United States)

    Lealdi, D.; Nurdiati, S.; Sopaheluwakan, A.

    2018-04-01

    Rainfall is an element of climate which is highly influential to the agricultural sector. Rain pattern and distribution highly determines the sustainability of agricultural activities. Therefore, information on rainfall is very useful for agriculture sector and farmers in anticipating the possibility of extreme events which often cause failures of agricultural production. This research aims to identify the biases from seasonal forecast products from ECMWF (European Centre for Medium-Range Weather Forecasts) rainfall forecast and to build a transfer function in order to correct the distribution biases as a new prediction model using quantile mapping approach. We apply this approach to the case of Bali Island, and as a result, the use of bias correction methods in correcting systematic biases from the model gives better results. The new prediction model obtained with this approach is better than ever. We found generally that during rainy season, the bias correction approach performs better than in dry season.

  4. Assessing Variability and Errors in Historical Runoff Forecasting with Physical Models and Alternative Data Sources

    Science.gov (United States)

    Penn, C. A.; Clow, D. W.; Sexstone, G. A.

    2017-12-01

    Water supply forecasts are an important tool for water resource managers in areas where surface water is relied on for irrigating agricultural lands and for municipal water supplies. Forecast errors, which correspond to inaccurate predictions of total surface water volume, can lead to mis-allocated water and productivity loss, thus costing stakeholders millions of dollars. The objective of this investigation is to provide water resource managers with an improved understanding of factors contributing to forecast error, and to help increase the accuracy of future forecasts. In many watersheds of the western United States, snowmelt contributes 50-75% of annual surface water flow and controls both the timing and volume of peak flow. Water supply forecasts from the Natural Resources Conservation Service (NRCS), National Weather Service, and similar cooperators use precipitation and snowpack measurements to provide water resource managers with an estimate of seasonal runoff volume. The accuracy of these forecasts can be limited by available snowpack and meteorological data. In the headwaters of the Rio Grande, NRCS produces January through June monthly Water Supply Outlook Reports. This study evaluates the accuracy of these forecasts since 1990, and examines what factors may contribute to forecast error. The Rio Grande headwaters has experienced recent changes in land cover from bark beetle infestation and a large wildfire, which can affect hydrological processes within the watershed. To investigate trends and possible contributing factors in forecast error, a semi-distributed hydrological model was calibrated and run to simulate daily streamflow for the period 1990-2015. Annual and seasonal watershed and sub-watershed water balance properties were compared with seasonal water supply forecasts. Gridded meteorological datasets were used to assess changes in the timing and volume of spring precipitation events that may contribute to forecast error. Additionally, a

  5. ECONOMIC FORECASTS BASED ON ECONOMETRIC MODELS USING EViews 5

    Directory of Open Access Journals (Sweden)

    Cornelia TomescuDumitrescu,

    2009-05-01

    Full Text Available The forecast of evolution of economic phenomena represent on the most the final objective of econometrics. It withal represent a real attempt of validity elaborate model. Unlike the forecasts based on the study of temporal series which have an recognizable inertial character the forecasts generated by econometric model with simultaneous equations are after to contour the future of ones of important economic variables toward the direct and indirect influences bring the bear on their about exogenous variables. For the relief of the calculus who the realization of the forecasts based on the econometric models its suppose is indicate the use of the specialized informatics programs. One of this is the EViews which is applied because it reduces significant the time who is destined of the econometric analysis and it assure a high accuracy of calculus and of the interpretation of results.

  6. A Comparative Study Of Stock Price Forecasting Using Nonlinear Models

    Directory of Open Access Journals (Sweden)

    Diteboho Xaba

    2017-03-01

    Full Text Available This study compared the in-sample forecasting accuracy of three forecasting nonlinear models namely: the Smooth Transition Regression (STR model, the Threshold Autoregressive (TAR model and the Markov-switching Autoregressive (MS-AR model. Nonlinearity tests were used to confirm the validity of the assumptions of the study. The study used model selection criteria, SBC to select the optimal lag order and for the selection of appropriate models. The Mean Square Error (MSE, Mean Absolute Error (MAE and Root Mean Square Error (RMSE served as the error measures in evaluating the forecasting ability of the models. The MS-AR models proved to perform well with lower error measures as compared to LSTR and TAR models in most cases.

  7. Probabilistic forecasting of the solar irradiance with recursive ARMA and GARCH models

    DEFF Research Database (Denmark)

    David, M.; Ramahatana, F.; Trombe, Pierre-Julien

    2016-01-01

    Forecasting of the solar irradiance is a key feature in order to increase the penetration rate of solar energy into the energy grids. Indeed, the anticipation of the fluctuations of the solar renewables allows a better management of the production means of electricity and a better operation...... sky index show some similarities with that of financial time series. The aim of this paper is to assess the performances of a commonly used combination of two linear models (ARMA and GARCH) in econometrics in order to provide probabilistic forecasts of solar irradiance. In addition, a recursive...... regarding the statistical distribution of the error, the reliability of the probabilistic forecasts stands in the same order of magnitude as other works done in the field of solar forecasting....

  8. Comparison on the forecast model of landfill surface

    International Nuclear Information System (INIS)

    Zhou Xiaozhi; Sang Shuxun; Cao Liwen; Ji Xiaoyan

    2010-01-01

    Using four large-scale simulated landfill equipments, indoor parallel simulation landfill experiment was carried out. By monitoring the cumulative settlement of MSW, comparable researches indicate the actual effects of 'empirical model' and 'biodegradation model' on landfill surface settlement forecast, and the optimization measures are proposed on the basis of model defects analysis. Research leaded to following results: To the short-term prediction of MSW settlement, two types of models all have satisfactory predictive validity. When performing medium and long-term prediction, 'empirical model' predicted a significant deviation from the actual, and the forecasting error of 'biodegradation model' is also gradually enlarge with the extending forecast period. For optimizing these two types of model, long-term surface settlement monitoring is fundamental method, and constantly modify the model parameters is the key according to the dynamic monitoring data. (authors)

  9. A complex autoregressive model and application to monthly temperature forecasts

    Directory of Open Access Journals (Sweden)

    X. Gu

    2005-11-01

    Full Text Available A complex autoregressive model was established based on the mathematic derivation of the least squares for the complex number domain which is referred to as the complex least squares. The model is different from the conventional way that the real number and the imaginary number are separately calculated. An application of this new model shows a better forecast than forecasts from other conventional statistical models, in predicting monthly temperature anomalies in July at 160 meteorological stations in mainland China. The conventional statistical models include an autoregressive model, where the real number and the imaginary number are separately disposed, an autoregressive model in the real number domain, and a persistence-forecast model.

  10. Application of Markov Model in Crude Oil Price Forecasting

    Directory of Open Access Journals (Sweden)

    Nuhu Isah

    2017-08-01

    Full Text Available Crude oil is an important energy commodity to mankind. Several causes have made crude oil prices to be volatile. The fluctuation of crude oil prices has affected many related sectors and stock market indices. Hence, forecasting the crude oil prices is essential to avoid the future prices of the non-renewable natural resources to rise. In this study, daily crude oil prices data was obtained from WTI dated 2 January to 29 May 2015. We used Markov Model (MM approach in forecasting the crude oil prices. In this study, the analyses were done using EViews and Maple software where the potential of this software in forecasting daily crude oil prices time series data was explored. Based on the study, we concluded that MM model is able to produce accurate forecast based on a description of history patterns in crude oil prices.

  11. 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.

  12. Wavelet regression model in forecasting crude oil price

    Science.gov (United States)

    Hamid, Mohd Helmie; Shabri, Ani

    2017-05-01

    This study presents the performance of wavelet multiple linear regression (WMLR) technique in daily crude oil forecasting. WMLR model was developed by integrating the discrete wavelet transform (DWT) and multiple linear regression (MLR) model. The original time series was decomposed to sub-time series with different scales by wavelet theory. Correlation analysis was conducted to assist in the selection of optimal decomposed components as inputs for the WMLR model. The daily WTI crude oil price series has been used in this study to test the prediction capability of the proposed model. The forecasting performance of WMLR model were also compared with regular multiple linear regression (MLR), Autoregressive Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) using root mean square errors (RMSE) and mean absolute errors (MAE). Based on the experimental results, it appears that the WMLR model performs better than the other forecasting technique tested in this study.

  13. Predictive Uncertainty Estimation in Water Demand Forecasting Using the Model Conditional Processor

    Directory of Open Access Journals (Sweden)

    Amos O. Anele

    2018-04-01

    Full Text Available In a previous paper, a number of potential models for short-term water demand (STWD prediction have been analysed to find the ones with the best fit. The results obtained in Anele et al. (2017 showed that hybrid models may be considered as the accurate and appropriate forecasting models for STWD prediction. However, such best single valued forecast does not guarantee reliable and robust decisions, which can be properly obtained via model uncertainty processors (MUPs. MUPs provide an estimate of the full predictive densities and not only the single valued expected prediction. Amongst other MUPs, the purpose of this paper is to use the multi-variate version of the model conditional processor (MCP, proposed by Todini (2008, to demonstrate how the estimation of the predictive probability conditional to a number of relatively good predictive models may improve our knowledge, thus reducing the predictive uncertainty (PU when forecasting into the unknown future. Through the MCP approach, the probability distribution of the future water demand can be assessed depending on the forecast provided by one or more deterministic forecasting models. Based on an average weekly data of 168 h, the probability density of the future demand is built conditional on three models’ predictions, namely the autoregressive-moving average (ARMA, feed-forward back propagation neural network (FFBP-NN and hybrid model (i.e., combined forecast from ARMA and FFBP-NN. The results obtained show that MCP may be effectively used for real-time STWD prediction since it brings out the PU connected to its forecast, and such information could help water utilities estimate the risk connected to a decision.

  14. Functional dynamic factor models with application to yield curve forecasting

    KAUST Repository

    Hays, Spencer

    2012-09-01

    Accurate forecasting of zero coupon bond yields for a continuum of maturities is paramount to bond portfolio management and derivative security pricing. Yet a universal model for yield curve forecasting has been elusive, and prior attempts often resulted in a trade-off between goodness of fit and consistency with economic theory. To address this, herein we propose a novel formulation which connects the dynamic factor model (DFM) framework with concepts from functional data analysis: a DFM with functional factor loading curves. This results in a model capable of forecasting functional time series. Further, in the yield curve context we show that the model retains economic interpretation. Model estimation is achieved through an expectation- maximization algorithm, where the time series parameters and factor loading curves are simultaneously estimated in a single step. Efficient computing is implemented and a data-driven smoothing parameter is nicely incorporated. We show that our model performs very well on forecasting actual yield data compared with existing approaches, especially in regard to profit-based assessment for an innovative trading exercise. We further illustrate the viability of our model to applications outside of yield forecasting.

  15. 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.

  16. Container Throughput Forecasting Using Dynamic Factor Analysis and ARIMAX Model

    Directory of Open Access Journals (Sweden)

    Marko Intihar

    2017-11-01

    Full Text Available The paper examines the impact of integration of macroeconomic indicators on the accuracy of container throughput time series forecasting model. For this purpose, a Dynamic factor analysis and AutoRegressive Integrated Moving-Average model with eXogenous inputs (ARIMAX are used. Both methodologies are integrated into a novel four-stage heuristic procedure. Firstly, dynamic factors are extracted from external macroeconomic indicators influencing the observed throughput. Secondly, the family of ARIMAX models of different orders is generated based on the derived factors. In the third stage, the diagnostic and goodness-of-fit testing is applied, which includes statistical criteria such as fit performance, information criteria, and parsimony. Finally, the best model is heuristically selected and tested on the real data of the Port of Koper. The results show that by applying macroeconomic indicators into the forecasting model, more accurate future throughput forecasts can be achieved. The model is also used to produce future forecasts for the next four years indicating a more oscillatory behaviour in (2018-2020. Hence, care must be taken concerning any bigger investment decisions initiated from the management side. It is believed that the proposed model might be a useful reinforcement of the existing forecasting module in the observed port.

  17. 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.

  18. Application of BP Neural Network Algorithm in Traditional Hydrological Model for Flood Forecasting

    Directory of Open Access Journals (Sweden)

    Jianjin Wang

    2017-01-01

    Full Text Available Flooding contributes to tremendous hazards every year; more accurate forecasting may significantly mitigate the damages and loss caused by flood disasters. Current hydrological models are either purely knowledge-based or data-driven. A combination of data-driven method (artificial neural networks in this paper and knowledge-based method (traditional hydrological model may booster simulation accuracy. In this study, we proposed a new back-propagation (BP neural network algorithm and applied it in the semi-distributed Xinanjiang (XAJ model. The improved hydrological model is capable of updating the flow forecasting error without losing the leading time. The proposed method was tested in a real case study for both single period corrections and real-time corrections. The results reveal that the proposed method could significantly increase the accuracy of flood forecasting and indicate that the global correction effect is superior to the second-order autoregressive correction method in real-time correction.

  19. A review of forecasting models for new products

    Directory of Open Access Journals (Sweden)

    Marta Mas-Machuca

    2014-02-01

    Full Text Available Purpose. The main objective of this article is to present an up-to-date review of new product forecasting techniques. Design/methodology/approach: A systematic review of forecasting journals was carried out using the ISI-Web of Knowledge database. Several articles were retrieved and examined, and forecasting techniques relevant to this study were selected and assessed. Findings: The strengths, weaknesses and applications of the main forecasting models are discussed to examine trends and set future challenges. Research limitations/implications: A theoretical reference framework for forecasting techniques classified into judgmental, consumer/market research, cause-effect and artificial intelligence is proposed. Future research can assess these models qualitatively. Practical implications: Companies are currently motivated to launch new products and thus attract new customers to expand their market share.  In order to reduce uncertainty and risk, many companies go to extra lengths to forecast sales accurately using several techniques. Originality/value: This article outlines new lines of research on the improvement of new product performance which will aid managers in decision making and allow companies to sustain their competitive advantages in this challenging world.

  20. Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting

    Directory of Open Access Journals (Sweden)

    Bijay Neupane

    2017-01-01

    Full Text Available Forecasting of electricity prices is important in deregulated electricity markets for all of the stakeholders: energy wholesalers, traders, retailers and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards the aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participates in forecasting 1-h ahead the price for each hour of a day. We propose two different strategies, namely, the Fixed Weight Method (FWM and the Varying Weight Method (VWM, for selecting each hour’s expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features extracted from the past electricity price data, weather data and calendar data. The proposed ensemble model offers better results than the Autoregressive Integrated Moving Average (ARIMA method, the Pattern Sequence-based Forecasting (PSF method and our previous work using Artificial Neural Networks (ANN alone on the datasets for New York, Australian and Spanish electricity markets.

  1. Short-Term State Forecasting-Based Optimal Voltage Regulation in Distribution Systems: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Rui; Jiang, Huaiguang; Zhang, Yingchen

    2017-05-17

    A novel short-term state forecasting-based optimal power flow (OPF) approach for distribution system voltage regulation is proposed in this paper. An extreme learning machine (ELM) based state forecaster is developed to accurately predict system states (voltage magnitudes and angles) in the near future. Based on the forecast system states, a dynamically weighted three-phase AC OPF problem is formulated to minimize the voltage violations with higher penalization on buses which are forecast to have higher voltage violations in the near future. By solving the proposed OPF problem, the controllable resources in the system are optimally coordinated to alleviate the potential severe voltage violations and improve the overall voltage profile. The proposed approach has been tested in a 12-bus distribution system and simulation results are presented to demonstrate the performance of the proposed approach.

  2. Forecasting Hourly Water Demands With Seasonal Autoregressive Models for Real-Time Application

    Science.gov (United States)

    Chen, Jinduan; Boccelli, Dominic L.

    2018-02-01

    Consumer water demands are not typically measured at temporal or spatial scales adequate to support real-time decision making, and recent approaches for estimating unobserved demands using observed hydraulic measurements are generally not capable of forecasting demands and uncertainty information. While time series modeling has shown promise for representing total system demands, these models have generally not been evaluated at spatial scales appropriate for representative real-time modeling. This study investigates the use of a double-seasonal time series model to capture daily and weekly autocorrelations to both total system demands and regional aggregated demands at a scale that would capture demand variability across a distribution system. Emphasis was placed on the ability to forecast demands and quantify uncertainties with results compared to traditional time series pattern-based demand models as well as nonseasonal and single-seasonal time series models. Additional research included the implementation of an adaptive-parameter estimation scheme to update the time series model when unobserved changes occurred in the system. For two case studies, results showed that (1) for the smaller-scale aggregated water demands, the log-transformed time series model resulted in improved forecasts, (2) the double-seasonal model outperformed other models in terms of forecasting errors, and (3) the adaptive adjustment of parameters during forecasting improved the accuracy of the generated prediction intervals. These results illustrate the capabilities of time series modeling to forecast both water demands and uncertainty estimates at spatial scales commensurate for real-time modeling applications and provide a foundation for developing a real-time integrated demand-hydraulic model.

  3. Volatility Forecasting Models and Market Co-Integration: A Study on South-East Asian Markets

    Directory of Open Access Journals (Sweden)

    Erie Febrian

    2014-11-01

    Full Text Available Volatility forecasting is an imperative research field in financial markets and crucial component in most financial decisions. Nevertheless, which model should be used to assess volatility remains a complex issue as different volatility models result in different volatility approximations. The concern becomes more complicated when one tries to use the forecasting for asset distribution and risk management purposes in the linked regional markets. This paper aims at observing the effectiveness of the contending models of statistical and econometric volatility forecasting in the three South-east Asian prominent capital markets, i.e. STI, KLSE, and JKSE. In this paper, we evaluate eleven different models based on two classes of evaluation measures, i.e. symmetric and asymmetric error statistics, following Kumar's (2006 framework. We employ 10-year data as in sample and 6-month data as out of sample to construct and test the models, consecutively. The resulting superior methods, which are selected based on the out of sample forecasts and some evaluation measures in the respective markets, are then used to assess the markets cointegration. We find that the best volatility forecasting models for JKSE, KLSE, and STI are GARCH (2,1, GARCH(3,1, and GARCH (1,1, respectively. We also find that international portfolio investors cannot benefit from diversification among these three equity markets as they are cointegrated.

  4. How informative are slip models for aftershock forecasting?

    Science.gov (United States)

    Bach, Christoph; Hainzl, Sebastian

    2013-04-01

    Coulomb stress changes (ΔCFS) have been recognized as a major trigger mechanism for earthquakes, in particular aftershock distributions and the spatial patterns of ΔCFS are often found to be correlated. However, the Coulomb stress calculations are based on slip inversions and the receiver fault mechanisms which both contain large uncertainties. In particular, slip inversions are usually non-unique and often differ strongly for the same earthquakes. Here we want to address the information content of those inversions with respect to aftershock forecasting. Therefore we compare the slip models to randomized fractal slip models which are only constrained by fault information and moment magnitude. The uncertainty of the aftershock mechanisms is considered by using many receiver fault orientations, and by calculating ΔCFS at several depth layers. The stress change is then converted into an aftershock probability map utilizing a clock advance model. To estimate the information content of the slip models, we use an Epidemic Type Aftershock Sequence (ETAS) model approach introduced by Bach and Hainzl (2012), where the spatial probability density of direct aftershocks is related to the ΔCFS calculations. Besides the directly triggered aftershocks, this approach also takes secondary aftershock triggering into account. We quantify our results by calculating the information gain of the randomized slip models relative to the corresponding published slip model. As case studies, we investigate the aftershock sequences of several well-known main shocks such as 1992 Landers, 1999 Hector Mine, 2004 Parkfield, 2002 Denali. First results show a huge difference in the information content of slip models. For some of the cases up to 90% of the random slip models are found to perform better than the originally published model, for some other cases only few random models are found performing better than the published slip model.

  5. Evaluation of the performance of DIAS ionospheric forecasting models

    Directory of Open Access Journals (Sweden)

    Tsagouri Ioanna

    2011-08-01

    Full Text Available Nowcasting and forecasting ionospheric products and services for the European region are regularly provided since August 2006 through the European Digital upper Atmosphere Server (DIAS, http://dias.space.noa.gr. Currently, DIAS ionospheric forecasts are based on the online implementation of two models: (i the solar wind driven autoregression model for ionospheric short-term forecast (SWIF, which combines historical and real-time ionospheric observations with solar-wind parameters obtained in real time at the L1 point from NASA ACE spacecraft, and (ii the geomagnetically correlated autoregression model (GCAM, which is a time series forecasting method driven by a synthetic geomagnetic index. In this paper we investigate the operational ability and the accuracy of both DIAS models carrying out a metrics-based evaluation of their performance under all possible conditions. The analysis was established on the systematic comparison between models’ predictions with actual observations obtained over almost one solar cycle (1998–2007 at four European ionospheric locations (Athens, Chilton, Juliusruh and Rome and on the comparison of the models’ performance against two simple prediction strategies, the median- and the persistence-based predictions during storm conditions. The results verify operational validity for both models and quantify their prediction accuracy under all possible conditions in support of operational applications but also of comparative studies in assessing or expanding the current ionospheric forecasting capabilities.

  6. Forecasting Performance of Asymmetric GARCH Stock Market Volatility Models

    Directory of Open Access Journals (Sweden)

    Hojin Lee

    2009-12-01

    Full Text Available We investigate the asymmetry between positive and negative returns in their effect on conditional variance of the stock market index and incorporate the characteristics to form an out-of-sample volatility forecast. Contrary to prior evidence, however, the results in this paper suggest that no asymmetric GARCH model is superior to basic GARCH(1,1 model. It is our prior knowledge that, for equity returns, it is unlikely that positive and negative shocks have the same impact on the volatility. In order to reflect this intuition, we implement three diagnostic tests for volatility models: the Sign Bias Test, the Negative Size Bias Test, and the Positive Size Bias Test and the tests against the alternatives of QGARCH and GJR-GARCH. The asymmetry test results indicate that the sign and the size of the unexpected return shock do not influence current volatility differently which contradicts our presumption that there are asymmetric effects in the stock market volatility. This result is in line with various diagnostic tests which are designed to determine whether the GARCH(1,1 volatility estimates adequately represent the data. The diagnostic tests in section 2 indicate that the GARCH(1,1 model for weekly KOSPI returns is robust to the misspecification test. We also investigate two representative asymmetric GARCH models, QGARCH and GJR-GARCH model, for our out-of-sample forecasting performance. The out-of-sample forecasting ability test reveals that no single model is clearly outperforming. It is seen that the GJR-GARCH and QGARCH model give mixed results in forecasting ability on all four criteria across all forecast horizons considered. Also, the predictive accuracy test of Diebold and Mariano based on both absolute and squared prediction errors suggest that the forecasts from the linear and asymmetric GARCH models need not be significantly different from each other.

  7. Air Quality Forecasts Using the NASA GEOS Model

    Science.gov (United States)

    Keller, Christoph A.; Knowland, K. Emma; Nielsen, Jon E.; Orbe, Clara; Ott, Lesley; Pawson, Steven; Saunders, Emily; Duncan, Bryan; Follette-Cook, Melanie; Liu, Junhua; hide

    2018-01-01

    We provide an introduction to a new high-resolution (0.25 degree) global composition forecast produced by NASA's Global Modeling and Assimilation office. The NASA Goddard Earth Observing System version 5 (GEOS-5) model has been expanded to provide global near-real-time forecasts of atmospheric composition at a horizontal resolution of 0.25 degrees (25 km). Previously, this combination of detailed chemistry and resolution was only provided by regional models. This system combines the operational GEOS-5 weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 11) to provide detailed chemical analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). The resolution of the forecasts is the highest resolution compared to current, publically-available global composition forecasts. Evaluation and validation of modeled trace gases and aerosols compared to surface and satellite observations will be presented for constituents relative to health air quality standards. Comparisons of modeled trace gases and aerosols against satellite observations show that the model produces realistic concentrations of atmospheric constituents in the free troposphere. Model comparisons against surface observations highlight the model's capability to capture the diurnal variability of air pollutants under a variety of meteorological conditions. The GEOS-5 composition forecasting system offers a new tool for scientists and the public health community, and is being developed jointly with several government and non-profit partners. Potential applications include air quality warnings, flight campaign planning and exposure studies using the archived analysis fields.

  8. 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.

  9. An interdisciplinary approach for earthquake modelling and forecasting

    Science.gov (United States)

    Han, P.; Zhuang, J.; Hattori, K.; Ogata, Y.

    2016-12-01

    Earthquake is one of the most serious disasters, which may cause heavy casualties and economic losses. Especially in the past two decades, huge/mega earthquakes have hit many countries. Effective earthquake forecasting (including time, location, and magnitude) becomes extremely important and urgent. To date, various heuristically derived algorithms have been developed for forecasting earthquakes. Generally, they can be classified into two types: catalog-based approaches and non-catalog-based approaches. Thanks to the rapid development of statistical seismology in the past 30 years, now we are able to evaluate the performances of these earthquake forecast approaches quantitatively. Although a certain amount of precursory information is available in both earthquake catalogs and non-catalog observations, the earthquake forecast is still far from satisfactory. In most case, the precursory phenomena were studied individually. An earthquake model that combines self-exciting and mutually exciting elements was developed by Ogata and Utsu from the Hawkes process. The core idea of this combined model is that the status of the event at present is controlled by the event itself (self-exciting) and all the external factors (mutually exciting) in the past. In essence, the conditional intensity function is a time-varying Poisson process with rate λ(t), which is composed of the background rate, the self-exciting term (the information from past seismic events), and the external excitation term (the information from past non-seismic observations). This model shows us a way to integrate the catalog-based forecast and non-catalog-based forecast. Against this background, we are trying to develop a new earthquake forecast model which combines catalog-based and non-catalog-based approaches.

  10. Short-Term Load Forecasting-Based Automatic Distribution Network Reconfiguration

    Energy Technology Data Exchange (ETDEWEB)

    Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

    2017-08-23

    In a traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of the load forecasting technique can provide an accurate prediction of the load power that will happen in a future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during a longer time period instead of using a snapshot of the load at the time when the reconfiguration happens; thus, the distribution system operator can use this information to better operate the system reconfiguration and achieve optimal solutions. This paper proposes a short-term load forecasting approach to automatically reconfigure distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with a forecaster based on support vector regression and parallel parameters optimization. The network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum amount of loss at the future time. The simulation results validate and evaluate the proposed approach.

  11. Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

    2017-07-26

    In the traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of load forecasting technique can provide accurate prediction of load power that will happen in future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during the longer time period instead of using the snapshot of load at the time when the reconfiguration happens, and thus it can provide information to the distribution system operator (DSO) to better operate the system reconfiguration to achieve optimal solutions. Thus, this paper proposes a short-term load forecasting based approach for automatically reconfiguring distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with support vector regression (SVR) based forecaster and parallel parameters optimization. And the network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum loss at the future time. The simulation results validate and evaluate the proposed approach.

  12. EXCHANGE-RATES FORECASTING: EXPONENTIAL SMOOTHING TECHNIQUES AND ARIMA MODELS

    Directory of Open Access Journals (Sweden)

    Dezsi Eva

    2011-07-01

    Full Text Available Exchange rates forecasting is, and has been a challenging task in finance. Statistical and econometrical models are widely used in analysis and forecasting of foreign exchange rates. This paper investigates the behavior of daily exchange rates of the Romanian Leu against the Euro, United States Dollar, British Pound, Japanese Yen, Chinese Renminbi and the Russian Ruble. Smoothing techniques are generated and compared with each other. These models include the Simple Exponential Smoothing technique, as the Double Exponential Smoothing technique, the Simple Holt-Winters, the Additive Holt-Winters, namely the Autoregressive Integrated Moving Average model.

  13. Predicting Power Outages Using Multi-Model Ensemble Forecasts

    Science.gov (United States)

    Cerrai, D.; Anagnostou, E. N.; Yang, J.; Astitha, M.

    2017-12-01

    Power outages affect every year millions of people in the United States, affecting the economy and conditioning the everyday life. An Outage Prediction Model (OPM) has been developed at the University of Connecticut for helping utilities to quickly restore outages and to limit their adverse consequences on the population. The OPM, operational since 2015, combines several non-parametric machine learning (ML) models that use historical weather storm simulations and high-resolution weather forecasts, satellite remote sensing data, and infrastructure and land cover data to predict the number and spatial distribution of power outages. A new methodology, developed for improving the outage model performances by combining weather- and soil-related variables using three different weather models (WRF 3.7, WRF 3.8 and RAMS/ICLAMS), will be presented in this study. First, we will present a performance evaluation of each model variable, by comparing historical weather analyses with station data or reanalysis over the entire storm data set. Hence, each variable of the new outage model version is extracted from the best performing weather model for that variable, and sensitivity tests are performed for investigating the most efficient variable combination for outage prediction purposes. Despite that the final variables combination is extracted from different weather models, this ensemble based on multi-weather forcing and multi-statistical model power outage prediction outperforms the currently operational OPM version that is based on a single weather forcing variable (WRF 3.7), because each model component is the closest to the actual atmospheric state.

  14. Numerical simulation of rainfall and temperature over Kenya using weather research and forecasting-environmental modelling system (WRF-EMS

    Directory of Open Access Journals (Sweden)

    Sagero Obaigwa Philip

    2016-01-01

    Full Text Available This paper focuses on one of the high resolution models used for weather forecasting at Kenya Meteorological Department (KMD. It reviews the skill and accuracy of the Weather Research and Forecasting (WRF - Environmental Modeling System (EMS model, in simulating weather over Kenya. The study period was March to May 2011, during the rainy season over Kenya. The model output was compared with the observed data from 27 synoptic stations spread over the study area, to determine the performance of the model in terms of its skill and accuracy in forecasting. The spatial distribution of rainfall and temperature showed that the WRF model was capable of reproducing the observed general pattern especially for temperature. The model has skill in forecasting both rainfall and temperature over the study area. However, the model may underestimate rainfall of more than 10 mm/day and displace its location and overestimate rainfall of less than 1 mm/day. Therefore, during the period of enhanced rainfall especially in the month of April and part of May the model forecast needs to be complemented by other models or forecasting methods before giving a forecast. There is need to improve its performance over the domain through review of the parameterization of small scale physical processes and more observed data need to be simulated into the model.

  15. Modeling of spatial dependence in wind power forecast uncertainty

    DEFF Research Database (Denmark)

    Papaefthymiou, George; Pinson, Pierre

    2008-01-01

    It is recognized today that short-term (up to 2-3 days ahead) probabilistic forecasts of wind power provide forecast users with a paramount information on the uncertainty of expected wind generation. When considering different areas covering a region, they are produced independently, and thus...... neglect the interdependence structure of prediction errors, induced by movement of meteorological fronts, or more generally by inertia of meteorological systems. This issue is addressed here by describing a method that permits to generate interdependent scenarios of wind generation for spatially...... distributed wind power production for specific look-ahead times. The approach is applied to the case of western Denmark split in 5 zones, for a total capacity of more than 2.1 GW. The interest of the methodology for improving the resolution of probabilistic forecasts, for a range of decision-making problems...

  16. Machine learning based switching model for electricity load forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Fan, Shu; Lee, Wei-Jen [Energy Systems Research Center, The University of Texas at Arlington, 416 S. College Street, Arlington, TX 76019 (United States); Chen, Luonan [Department of Electronics, Information and Communication Engineering, Osaka Sangyo University, 3-1-1 Nakagaito, Daito, Osaka 574-0013 (Japan)

    2008-06-15

    In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma. (author)

  17. Machine learning based switching model for electricity load forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Fan Shu [Energy Systems Research Center, University of Texas at Arlington, 416 S. College Street, Arlington, TX 76019 (United States); Chen Luonan [Department of Electronics, Information and Communication Engineering, Osaka Sangyo University, 3-1-1 Nakagaito, Daito, Osaka 574-0013 (Japan); Lee, Weijen [Energy Systems Research Center, University of Texas at Arlington, 416 S. College Street, Arlington, TX 76019 (United States)], E-mail: wlee@uta.edu

    2008-06-15

    In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma.

  18. Machine learning based switching model for electricity load forecasting

    International Nuclear Information System (INIS)

    Fan Shu; Chen Luonan; Lee, Weijen

    2008-01-01

    In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma

  19. Forecasting distributional responses of limber pine to climate change at management-relevant scales in Rocky Mountain National Park.

    Directory of Open Access Journals (Sweden)

    William B Monahan

    Full Text Available Resource managers at parks and other protected areas are increasingly expected to factor climate change explicitly into their decision making frameworks. However, most protected areas are small relative to the geographic ranges of species being managed, so forecasts need to consider local adaptation and community dynamics that are correlated with climate and affect distributions inside protected area boundaries. Additionally, niche theory suggests that species' physiological capacities to respond to climate change may be underestimated when forecasts fail to consider the full breadth of climates occupied by the species rangewide. Here, using correlative species distribution models that contrast estimates of climatic sensitivity inferred from the two spatial extents, we quantify the response of limber pine (Pinus flexilis to climate change in Rocky Mountain National Park (Colorado, USA. Models are trained locally within the park where limber pine is the community dominant tree species, a distinct structural-compositional vegetation class of interest to managers, and also rangewide, as suggested by niche theory. Model forecasts through 2100 under two representative concentration pathways (RCP 4.5 and 8.5 W/m(2 show that the distribution of limber pine in the park is expected to move upslope in elevation, but changes in total and core patch area remain highly uncertain. Most of this uncertainty is biological, as magnitudes of projected change are considerably more variable between the two spatial extents used in model training than they are between RCPs, and novel future climates only affect local model predictions associated with RCP 8.5 after 2091. Combined, these results illustrate the importance of accounting for unknowns in species' climatic sensitivities when forecasting distributional scenarios that are used to inform management decisions. We discuss how our results for limber pine may be interpreted in the context of climate change

  20. Lake Michigan lake trout PCB model forecast post audit

    Science.gov (United States)

    Scenario forecasts for total PCBs in Lake Michigan (LM) lake trout were conducted using the linked LM2-Toxics and LM Food Chain models, supported by a suite of additional LM models. Efforts were conducted under the Lake Michigan Mass Balance Study and the post audit represents th...

  1. Linking seasonal climate forecasts with crop models in Iberian Peninsula

    Science.gov (United States)

    Capa, Mirian; Ines, Amor; Baethgen, Walter; Rodriguez-Fonseca, Belen; Han, Eunjin; Ruiz-Ramos, Margarita

    2015-04-01

    Translating seasonal climate forecasts into agricultural production forecasts could help to establish early warning systems and to design crop management adaptation strategies that take advantage of favorable conditions or reduce the effect of adverse conditions. In this study, we use seasonal rainfall forecasts and crop models to improve predictability of wheat yield in the Iberian Peninsula (IP). Additionally, we estimate economic margins and production risks associated with extreme scenarios of seasonal rainfall forecast. This study evaluates two methods for disaggregating seasonal climate forecasts into daily weather data: 1) a stochastic weather generator (CondWG), and 2) a forecast tercile resampler (FResampler). Both methods were used to generate 100 (with FResampler) and 110 (with CondWG) weather series/sequences for three scenarios of seasonal rainfall forecasts. Simulated wheat yield is computed with the crop model CERES-wheat (Ritchie and Otter, 1985), which is included in Decision Support System for Agrotechnology Transfer (DSSAT v.4.5, Hoogenboom et al., 2010). Simulations were run at two locations in northeastern Spain where the crop model was calibrated and validated with independent field data. Once simulated yields were obtained, an assessment of farmer's gross margin for different seasonal climate forecasts was accomplished to estimate production risks under different climate scenarios. This methodology allows farmers to assess the benefits and risks of a seasonal weather forecast in IP prior to the crop growing season. The results of this study may have important implications on both, public (agricultural planning) and private (decision support to farmers, insurance companies) sectors. Acknowledgements Research by M. Capa-Morocho has been partly supported by a PICATA predoctoral fellowship of the Moncloa Campus of International Excellence (UCM-UPM) and MULCLIVAR project (CGL2012-38923-C02-02) References Hoogenboom, G. et al., 2010. The Decision

  2. Obtaining high-resolution stage forecasts by coupling large-scale hydrologic models with sensor data

    Science.gov (United States)

    Fries, K. J.; Kerkez, B.

    2017-12-01

    We investigate how "big" quantities of distributed sensor data can be coupled with a large-scale hydrologic model, in particular the National Water Model (NWM), to obtain hyper-resolution forecasts. The recent launch of the NWM provides a great example of how growing computational capacity is enabling a new generation of massive hydrologic models. While the NWM spans an unprecedented spatial extent, there remain many questions about how to improve forecast at the street-level, the resolution at which many stakeholders make critical decisions. Further, the NWM runs on supercomputers, so water managers who may have access to their own high-resolution measurements may not readily be able to assimilate them into the model. To that end, we ask the question: how can the advances of the large-scale NWM be coupled with new local observations to enable hyper-resolution hydrologic forecasts? A methodology is proposed whereby the flow forecasts of the NWM are directly mapped to high-resolution stream levels using Dynamical System Identification. We apply the methodology across a sensor network of 182 gages in Iowa. Of these sites, approximately one third have shown to perform well in high-resolution flood forecasting when coupled with the outputs of the NWM. The quality of these forecasts is characterized using Principal Component Analysis and Random Forests to identify where the NWM may benefit from new sources of local observations. We also discuss how this approach can help municipalities identify where they should place low-cost sensors to most benefit from flood forecasts of the NWM.

  3. Sub-seasonal-to-seasonal Reservoir Inflow Forecast using Bayesian Hierarchical Hidden Markov Model

    Science.gov (United States)

    Mukhopadhyay, S.; Arumugam, S.

    2017-12-01

    Sub-seasonal-to-seasonal (S2S) (15-90 days) streamflow forecasting is an emerging area of research that provides seamless information for reservoir operation from weather time scales to seasonal time scales. From an operational perspective, sub-seasonal inflow forecasts are highly valuable as these enable water managers to decide short-term releases (15-30 days), while holding water for seasonal needs (e.g., irrigation and municipal supply) and to meet end-of-the-season target storage at a desired level. We propose a Bayesian Hierarchical Hidden Markov Model (BHHMM) to develop S2S inflow forecasts for the Tennessee Valley Area (TVA) reservoir system. Here, the hidden states are predicted by relevant indices that influence the inflows at S2S time scale. The hidden Markov model also captures the both spatial and temporal hierarchy in predictors that operate at S2S time scale with model parameters being estimated as a posterior distribution using a Bayesian framework. We present our work in two steps, namely single site model and multi-site model. For proof of concept, we consider inflows to Douglas Dam, Tennessee, in the single site model. For multisite model we consider reservoirs in the upper Tennessee valley. Streamflow forecasts are issued and updated continuously every day at S2S time scale. We considered precipitation forecasts obtained from NOAA Climate Forecast System (CFSv2) GCM as predictors for developing S2S streamflow forecasts along with relevant indices for predicting hidden states. Spatial dependence of the inflow series of reservoirs are also preserved in the multi-site model. To circumvent the non-normality of the data, we consider the HMM in a Generalized Linear Model setting. Skill of the proposed approach is tested using split sample validation against a traditional multi-site canonical correlation model developed using the same set of predictors. From the posterior distribution of the inflow forecasts, we also highlight different system behavior

  4. Forecast model of safety economy contribution rate of China

    Institute of Scientific and Technical Information of China (English)

    LIU Li-jun; SHI Shi-liang

    2005-01-01

    It is the rational and exact computation of the safety economy contribution rate that has the far-reaching realistic meaning to the improvement of society cognition to safety and the investment to the nation safety and the national macro-safety decision-makings. The accurate function between safety inputs and outputs was obtained through a founded econometric model. Then the forecasted safety economy contribution rate is 3.01% and the forecasted ratio between safety inputs and outputs is 1:1.81 in China in 2005. And the model accords with the practice of China and the results are satisfying.

  5. A national econometric forecasting model of the dental sector.

    Science.gov (United States)

    Feldstein, P J; Roehrig, C S

    1980-01-01

    The Econometric Model of the the Dental Sector forecasts a broad range of dental sector variables, including dental care prices; the amount of care produced and consumed; employment of hygienists, dental assistants, and clericals; hours worked by dentists; dental incomes; and number of dentists. These forecasts are based upon values specified by the user for the various factors which help determine the supply an demand for dental care, such as the size of the population, per capita income, the proportion of the population covered by private dental insurance, the cost of hiring clericals and dental assistants, and relevant government policies. In a test of its reliability, the model forecast dental sector behavior quite accurately for the period 1971 through 1977. PMID:7461974

  6. Modelling and Forecasting Stock Price Movements with Serially Dependent Determinants

    Directory of Open Access Journals (Sweden)

    Rasika Yatigammana

    2018-05-01

    Full Text Available The direction of price movements are analysed under an ordered probit framework, recognising the importance of accounting for discreteness in price changes. By extending the work of Hausman et al. (1972 and Yang and Parwada (2012,This paper focuses on improving the forecast performance of the model while infusing a more practical perspective by enhancing flexibility. This is achieved by extending the existing framework to generate short term multi period ahead forecasts for better decision making, whilst considering the serial dependence structure. This approach enhances the flexibility and adaptability of the model to future price changes, particularly targeting risk minimisation. Empirical evidence is provided, based on seven stocks listed on the Australian Securities Exchange (ASX. The prediction success varies between 78 and 91 per cent for in-sample and out-of-sample forecasts for both the short term and long term.

  7. Ionospheric scintillation forecasting model based on NN-PSO technique

    Science.gov (United States)

    Sridhar, M.; Venkata Ratnam, D.; Padma Raju, K.; Sai Praharsha, D.; Saathvika, K.

    2017-09-01

    The forecasting and modeling of ionospheric scintillation effects are crucial for precise satellite positioning and navigation applications. In this paper, a Neural Network model, trained using Particle Swarm Optimization (PSO) algorithm, has been implemented for the prediction of amplitude scintillation index (S4) observations. The Global Positioning System (GPS) and Ionosonde data available at Darwin, Australia (12.4634° S, 130.8456° E) during 2013 has been considered. The correlation analysis between GPS S4 and Ionosonde drift velocities (hmf2 and fof2) data has been conducted for forecasting the S4 values. The results indicate that forecasted S4 values closely follow the measured S4 values for both the quiet and disturbed conditions. The outcome of this work will be useful for understanding the ionospheric scintillation phenomena over low latitude regions.

  8. Computer aided planning of distribution systems and connection with medium term load forecast

    International Nuclear Information System (INIS)

    di Salvatore, F.; Grattieri, W.; Insinga, F.; Malafarina, L.; Mazzoni, M.; Nicola, G.

    1990-01-01

    In order to perform planning studies on HV (40-l50 kV), MV and LV networks, ENEL (Italian Electricity Board) has developed a computation system composed of a set of integrated programs which utilize the information stored in several data bases, with the aim of: providing energy consumption forecasts for each area of the country; transferring consumption for each area to the distribution network nodes and to evaluating the electric demand by using a statistical power/energy correlation model; analyzing several network development alternatives and selecting the optimum development plan by comparing the overall costs (investments, operation, risk). In order to make its utilization by planners easier, the computation system will be operated with interactive and graphic procedures made available by the use of graphic work stations. This report describes the main objectives and basic hypotheses assumed in the preparation of the computation system, as well as, the system's general architecture

  9. Computer aided planning of distribution systems and connection with medium term load forecast

    Energy Technology Data Exchange (ETDEWEB)

    di Salvatore, F; Grattieri, W; Insinga, F; Malafarina, L; Mazzoni, M; Nicola, G

    1991-12-31

    In order to perform planning studies on HV (40-l50 kV), MV and LV networks, ENEL (Italian Electricity Board) has developed a computation system composed of a set of integrated programs which utilize the information stored in several data bases, with the aim of: providing energy consumption forecasts for each area of the country; transfering consumption for each area to the distribution network nodes and to evaluating the electric demand by using a statistical power/energy correlation model; analyzing several network development alternatives and selecting the optimum development plan by comparing the overall costs (investments, operation, risk). In order to make its utilization by planners easier, the computation system will be operated with interactive and graphic procedures made available by the use of graphic work stations. This report describes the main objectives and basic hypotheses assumed in the preparation of the computation system, as well as, the system`s general architecture.

  10. FORECASTING MODEL OF GHG EMISSION IN MANUFACTURING SECTORS OF THAILAND

    Directory of Open Access Journals (Sweden)

    Pruethsan Sutthichaimethee

    2017-01-01

    Full Text Available This study aims to analyze the modeling and forecasting the GHG emission of energy consumption in manufacturing sectors. The scope of the study is to analysis energy consumption and forecasting GHG emission of energy consumption for the next 10 years (2016-2025 and 25 years (2016-2040 by using ARIMAX model from the Input-output table of Thailand. The result shows that iron and steel has the highest value of energy consumption and followed by cement, fluorite, air transport, road freight transport, hotels and places of loading, coal and lignite, petrochemical products, other manufacturing, road passenger transport, respectively. The prediction results show that these models are effective in forecasting by measured by using RMSE, MAE, and MAPE. The results forecast of each model is as follows: 1 Model 1(2,1,1 shows that GHG emission will be increasing steadily and increasing at 25.17% by the year 2025 in comparison to 2016. 2 Model 2 (2,1,2 shows that GHG emission will be rising steadily and increasing at 41.51% by the year 2040 in comparison to 2016.

  11. Using Bayes Model Averaging for Wind Power Forecasts

    Science.gov (United States)

    Preede Revheim, Pål; Beyer, Hans Georg

    2014-05-01

    For operational purposes predictions of the forecasts of the lumped output of groups of wind farms spread over larger geographic areas will often be of interest. A naive approach is to make forecasts for each individual site and sum them up to get the group forecast. It is however well documented that a better choice is to use a model that also takes advantage of spatial smoothing effects. It might however be the case that some sites tends to more accurately reflect the total output of the region, either in general or for certain wind directions. It will then be of interest giving these a greater influence over the group forecast. Bayesian model averaging (BMA) is a statistical post-processing method for producing probabilistic forecasts from ensembles. Raftery et al. [1] show how BMA can be used for statistical post processing of forecast ensembles, producing PDFs of future weather quantities. The BMA predictive PDF of a future weather quantity is a weighted average of the ensemble members' PDFs, where the weights can be interpreted as posterior probabilities and reflect the ensemble members' contribution to overall forecasting skill over a training period. In Revheim and Beyer [2] the BMA procedure used in Sloughter, Gneiting and Raftery [3] were found to produce fairly accurate PDFs for the future mean wind speed of a group of sites from the single sites wind speeds. However, when the procedure was attempted applied to wind power it resulted in either problems with the estimation of the parameters (mainly caused by longer consecutive periods of no power production) or severe underestimation (mainly caused by problems with reflecting the power curve). In this paper the problems that arose when applying BMA to wind power forecasting is met through two strategies. First, the BMA procedure is run with a combination of single site wind speeds and single site wind power production as input. This solves the problem with longer consecutive periods where the input data

  12. Using size distribution analysis to forecast natural gas resources in Asia Pacific

    International Nuclear Information System (INIS)

    Aguilera, Roberto F.; Ripple, Ronald D.

    2011-01-01

    Highlights: → We estimate the total endowment of conventional natural gas in Asia Pacific. → Includes volumes in previously unassessed provinces. → Endowment distributed across countries to show where volumes are most likely to be found. → A breakdown between offshore versus onshore resources is also estimated. → We find there is a significant natural gas endowment in the region. -- Abstract: Increasing energy consumption in Asia Pacific will largely be met by fossil fuels. Natural gas production in the region presently ranks behind that of oil and coal. However, the abundance of gas could lead to a significant gas market share increase in the energy mix. The purpose of this paper is to estimate the total endowment of conventional gas in Asia Pacific. This is carried out with a Variable Shape Distribution (VSD) model that forecasts volumes in provinces that have not been previously evaluated. The endowment is then distributed across countries to show where volumes are most likely to be found. A breakdown between offshore versus onshore resources is also estimated. The results of the analysis show there is a significant gas endowment. The estimated distribution across countries and onshore/offshore areas provides insight into the relative economics of gas production, as well as a basis for potential investment decisions. With appropriate energy policies, it may be possible to tap the vast gas potential in Asia Pacific. Considering gas may be the most abundant, inexpensive, and clean fossil fuel, the outcome would be increased energy security and a low carbon economy.

  13. Hierarchical species distribution models

    Science.gov (United States)

    Hefley, Trevor J.; Hooten, Mevin B.

    2016-01-01

    Determining the distribution pattern of a species is important to increase scientific knowledge, inform management decisions, and conserve biodiversity. To infer spatial and temporal patterns, species distribution models have been developed for use with many sampling designs and types of data. Recently, it has been shown that count, presence-absence, and presence-only data can be conceptualized as arising from a point process distribution. Therefore, it is important to understand properties of the point process distribution. We examine how the hierarchical species distribution modeling framework has been used to incorporate a wide array of regression and theory-based components while accounting for the data collection process and making use of auxiliary information. The hierarchical modeling framework allows us to demonstrate how several commonly used species distribution models can be derived from the point process distribution, highlight areas of potential overlap between different models, and suggest areas where further research is needed.

  14. Model for Adjustment of Aggregate Forecasts using Fuzzy Logic

    Directory of Open Access Journals (Sweden)

    Taracena–Sanz L. F.

    2010-07-01

    Full Text Available This research suggests a contribution in the implementation of forecasting models. The proposed model is developed with the aim to fit the projection of demand to surroundings of firms, and this is based on three considerations that cause that in many cases the forecasts of the demand are different from reality, such as: 1 one of the problems most difficult to model in the forecasts is the uncertainty related to the information available; 2 the methods traditionally used by firms for the projection of demand mainly are based on past behavior of the market (historical demand; and 3 these methods do not consider in their analysis the factors that are influencing so that the observed behaviour occurs. Therefore, the proposed model is based on the implementation of Fuzzy Logic, integrating the main variables that affect the behavior of market demand, and which are not considered in the classical statistical methods. The model was applied to a bottling of carbonated beverages, and with the adjustment of the projection of demand a more reliable forecast was obtained.

  15. Research on light rail electric load forecasting based on ARMA model

    Science.gov (United States)

    Huang, Yifan

    2018-04-01

    The article compares a variety of time series models and combines the characteristics of power load forecasting. Then, a light load forecasting model based on ARMA model is established. Based on this model, a light rail system is forecasted. The prediction results show that the accuracy of the model prediction is high.

  16. Time series modelling and forecasting of emergency department overcrowding.

    Science.gov (United States)

    Kadri, Farid; Harrou, Fouzi; Chaabane, Sondès; Tahon, Christian

    2014-09-01

    Efficient management of patient flow (demand) in emergency departments (EDs) has become an urgent issue for many hospital administrations. Today, more and more attention is being paid to hospital management systems to optimally manage patient flow and to improve management strategies, efficiency and safety in such establishments. To this end, EDs require significant human and material resources, but unfortunately these are limited. Within such a framework, the ability to accurately forecast demand in emergency departments has considerable implications for hospitals to improve resource allocation and strategic planning. The aim of this study was to develop models for forecasting daily attendances at the hospital emergency department in Lille, France. The study demonstrates how time-series analysis can be used to forecast, at least in the short term, demand for emergency services in a hospital emergency department. The forecasts were based on daily patient attendances at the paediatric emergency department in Lille regional hospital centre, France, from January 2012 to December 2012. An autoregressive integrated moving average (ARIMA) method was applied separately to each of the two GEMSA categories and total patient attendances. Time-series analysis was shown to provide a useful, readily available tool for forecasting emergency department demand.

  17. Comparison of Conventional and ANN Models for River Flow Forecasting

    Science.gov (United States)

    Jain, A.; Ganti, R.

    2011-12-01

    Hydrological models are useful in many water resources applications such as flood control, irrigation and drainage, hydro power generation, water supply, erosion and sediment control, etc. Estimates of runoff are needed in many water resources planning, design development, operation and maintenance activities. River flow is generally estimated using time series or rainfall-runoff models. Recently, soft artificial intelligence tools such as Artificial Neural Networks (ANNs) have become popular for research purposes but have not been extensively adopted in operational hydrological forecasts. There is a strong need to develop ANN models based on real catchment data and compare them with the conventional models. In this paper, a comparative study has been carried out for river flow forecasting using the conventional and ANN models. Among the conventional models, multiple linear, and non linear regression, and time series models of auto regressive (AR) type have been developed. Feed forward neural network model structure trained using the back propagation algorithm, a gradient search method, was adopted. The daily river flow data derived from Godavari Basin @ Polavaram, Andhra Pradesh, India have been employed to develop all the models included here. Two inputs, flows at two past time steps, (Q(t-1) and Q(t-2)) were selected using partial auto correlation analysis for forecasting flow at time t, Q(t). A wide range of error statistics have been used to evaluate the performance of all the models developed in this study. It has been found that the regression and AR models performed comparably, and the ANN model performed the best amongst all the models investigated in this study. It is concluded that ANN model should be adopted in real catchments for hydrological modeling and forecasting.

  18. Earthquake and failure forecasting in real-time: A Forecasting Model Testing Centre

    Science.gov (United States)

    Filgueira, Rosa; Atkinson, Malcolm; Bell, Andrew; Main, Ian; Boon, Steven; Meredith, Philip

    2013-04-01

    Across Europe there are a large number of rock deformation laboratories, each of which runs many experiments. Similarly there are a large number of theoretical rock physicists who develop constitutive and computational models both for rock deformation and changes in geophysical properties. Here we consider how to open up opportunities for sharing experimental data in a way that is integrated with multiple hypothesis testing. We present a prototype for a new forecasting model testing centre based on e-infrastructures for capturing and sharing data and models to accelerate the Rock Physicist (RP) research. This proposal is triggered by our work on data assimilation in the NERC EFFORT (Earthquake and Failure Forecasting in Real Time) project, using data provided by the NERC CREEP 2 experimental project as a test case. EFFORT is a multi-disciplinary collaboration between Geoscientists, Rock Physicists and Computer Scientist. Brittle failure of the crust is likely to play a key role in controlling the timing of a range of geophysical hazards, such as volcanic eruptions, yet the predictability of brittle failure is unknown. Our aim is to provide a facility for developing and testing models to forecast brittle failure in experimental and natural data. Model testing is performed in real-time, verifiably prospective mode, in order to avoid selection biases that are possible in retrospective analyses. The project will ultimately quantify the predictability of brittle failure, and how this predictability scales from simple, controlled laboratory conditions to the complex, uncontrolled real world. Experimental data are collected from controlled laboratory experiments which includes data from the UCL Laboratory and from Creep2 project which will undertake experiments in a deep-sea laboratory. We illustrate the properties of the prototype testing centre by streaming and analysing realistically noisy synthetic data, as an aid to generating and improving testing methodologies in

  19. Adaptation of Mesoscale Weather Models to Local Forecasting

    Science.gov (United States)

    Manobianco, John T.; Taylor, Gregory E.; Case, Jonathan L.; Dianic, Allan V.; Wheeler, Mark W.; Zack, John W.; Nutter, Paul A.

    2003-01-01

    Methodologies have been developed for (1) configuring mesoscale numerical weather-prediction models for execution on high-performance computer workstations to make short-range weather forecasts for the vicinity of the Kennedy Space Center (KSC) and the Cape Canaveral Air Force Station (CCAFS) and (2) evaluating the performances of the models as configured. These methodologies have been implemented as part of a continuing effort to improve weather forecasting in support of operations of the U.S. space program. The models, methodologies, and results of the evaluations also have potential value for commercial users who could benefit from tailoring their operations and/or marketing strategies based on accurate predictions of local weather. More specifically, the purpose of developing the methodologies for configuring the models to run on computers at KSC and CCAFS is to provide accurate forecasts of winds, temperature, and such specific thunderstorm-related phenomena as lightning and precipitation. The purpose of developing the evaluation methodologies is to maximize the utility of the models by providing users with assessments of the capabilities and limitations of the models. The models used in this effort thus far include the Mesoscale Atmospheric Simulation System (MASS), the Regional Atmospheric Modeling System (RAMS), and the National Centers for Environmental Prediction Eta Model ( Eta for short). The configuration of the MASS and RAMS is designed to run the models at very high spatial resolution and incorporate local data to resolve fine-scale weather features. Model preprocessors were modified to incorporate surface, ship, buoy, and rawinsonde data as well as data from local wind towers, wind profilers, and conventional or Doppler radars. The overall evaluation of the MASS, Eta, and RAMS was designed to assess the utility of these mesoscale models for satisfying the weather-forecasting needs of the U.S. space program. The evaluation methodology includes

  20. A systematic review of health manpower forecasting models.

    NARCIS (Netherlands)

    Martins-Coelho, G.; Greuningen, M. van; Barros, H.; Batenburg, R.

    2011-01-01

    Context: Health manpower planning (HMP) aims at matching health manpower (HM) supply to the population’s health requirements. To achieve this, HMP needs information on future HM supply and requirement (S&R). This is estimated by several different forecasting models (FMs). In this paper, we review

  1. Inflation, Forecast Intervals and Long Memory Regression Models

    NARCIS (Netherlands)

    C.S. Bos (Charles); Ph.H.B.F. Franses (Philip Hans); M. Ooms (Marius)

    2001-01-01

    textabstractWe examine recursive out-of-sample forecasting of monthly postwar U.S. core inflation and log price levels. We use the autoregressive fractionally integrated moving average model with explanatory variables (ARFIMAX). Our analysis suggests a significant explanatory power of leading

  2. Inflation, Forecast Intervals and Long Memory Regression Models

    NARCIS (Netherlands)

    Ooms, M.; Bos, C.S.; Franses, P.H.

    2003-01-01

    We examine recursive out-of-sample forecasting of monthly postwar US core inflation and log price levels. We use the autoregressive fractionally integrated moving average model with explanatory variables (ARFIMAX). Our analysis suggests a significant explanatory power of leading indicators

  3. A stochastic model for forecast consumption in master scheduling

    NARCIS (Netherlands)

    Weeda, P.J.; Weeda, P.J.

    1994-01-01

    This paper describes a stochastic model for the reduction of the initial forecast in the Master Schedule (MS) of an MRP system during progress of time by the acceptance of customer orders. Results are given for the expectation and variance of the number of yet unknown deliveries as a function of

  4. Development of a forecast model for global air traffic emissions

    Energy Technology Data Exchange (ETDEWEB)

    Schaefer, Martin

    2012-07-01

    The thesis describes the methodology and results of a simulation model that quantifies fuel consumption and emissions of civil air traffic. Besides covering historical emissions, the model aims at forecasting emissions in the medium-term future. For this purpose, simulation models of aircraft and engine types are used in combination with a database of global flight movements and assumptions about traffic growth, fleet rollover and operational aspects. Results from an application of the model include emissions of scheduled air traffic for the years 2000 to 2010 as well as forecasted emissions until the year 2030. In a baseline scenario of the forecast, input assumptions (e.g. traffic growth rates) are in line with predictions by the aircraft industry. Considering the effects of advanced technologies of the short-term and medium-term future, the forecast focusses on fuel consumption and emissions of nitric oxides. Calculations for historical air traffic additionally cover emissions of carbon monoxide, unburned hydrocarbons and soot. Results are validated against reference data including studies by the International Civil Aviation Organization (ICAO) and simulation results from international research projects. (orig.)

  5. Development of Parallel Code for the Alaska Tsunami Forecast Model

    Science.gov (United States)

    Bahng, B.; Knight, W. R.; Whitmore, P.

    2014-12-01

    The Alaska Tsunami Forecast Model (ATFM) is a numerical model used to forecast propagation and inundation of tsunamis generated by earthquakes and other means in both the Pacific and Atlantic Oceans. At the U.S. National Tsunami Warning Center (NTWC), the model is mainly used in a pre-computed fashion. That is, results for hundreds of hypothetical events are computed before alerts, and are accessed and calibrated with observations during tsunamis to immediately produce forecasts. ATFM uses the non-linear, depth-averaged, shallow-water equations of motion with multiply nested grids in two-way communications between domains of each parent-child pair as waves get closer to coastal waters. Even with the pre-computation the task becomes non-trivial as sub-grid resolution gets finer. Currently, the finest resolution Digital Elevation Models (DEM) used by ATFM are 1/3 arc-seconds. With a serial code, large or multiple areas of very high resolution can produce run-times that are unrealistic even in a pre-computed approach. One way to increase the model performance is code parallelization used in conjunction with a multi-processor computing environment. NTWC developers have undertaken an ATFM code-parallelization effort to streamline the creation of the pre-computed database of results with the long term aim of tsunami forecasts from source to high resolution shoreline grids in real time. Parallelization will also permit timely regeneration of the forecast model database with new DEMs; and, will make possible future inclusion of new physics such as the non-hydrostatic treatment of tsunami propagation. The purpose of our presentation is to elaborate on the parallelization approach and to show the compute speed increase on various multi-processor systems.

  6. Flood forecasting and uncertainty of precipitation forecasts

    International Nuclear Information System (INIS)

    Kobold, Mira; Suselj, Kay

    2004-01-01

    The timely and accurate flood forecasting is essential for the reliable flood warning. The effectiveness of flood warning is dependent on the forecast accuracy of certain physical parameters, such as the peak magnitude of the flood, its timing, location and duration. The conceptual rainfall - runoff models enable the estimation of these parameters and lead to useful operational forecasts. The accurate rainfall is the most important input into hydrological models. The input for the rainfall can be real time rain-gauges data, or weather radar data, or meteorological forecasted precipitation. The torrential nature of streams and fast runoff are characteristic for the most of the Slovenian rivers. Extensive damage is caused almost every year- by rainstorms affecting different regions of Slovenia' The lag time between rainfall and runoff is very short for Slovenian territory and on-line data are used only for now casting. Forecasted precipitations are necessary for hydrological forecast for some days ahead. ECMWF (European Centre for Medium-Range Weather Forecasts) gives general forecast for several days ahead while more detailed precipitation data with limited area ALADIN/Sl model are available for two days ahead. There is a certain degree of uncertainty using such precipitation forecasts based on meteorological models. The variability of precipitation is very high in Slovenia and the uncertainty of ECMWF predicted precipitation is very large for Slovenian territory. ECMWF model can predict precipitation events correctly, but underestimates amount of precipitation in general The average underestimation is about 60% for Slovenian region. The predictions of limited area ALADIN/Si model up to; 48 hours ahead show greater applicability in hydrological forecasting. The hydrological models are sensitive to precipitation input. The deviation of runoff is much bigger than the rainfall deviation. Runoff to rainfall error fraction is about 1.6. If spatial and time distribution

  7. 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)

  8. 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)

  9. 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

  10. 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)

  11. The Challenge of Forecasting Metropolitan Growth: Urban Characteristics Based Models versus Regional Dummy Based Models

    OpenAIRE

    NA

    2005-01-01

    This paper presents a study of errors in forecasting the population of Metropolitan Statistical Areas and the Primary MSAs of Consolidated Metropolitan Statistical Areas and New England MAs. The forecasts are for the year 2000 and are based on a semi-structural model estimated by Mills and Lubelle using 1970 to 1990 census data on population, employment and relative real wages. This model allows the testing of regional effects on population and employment growth. The year 2000 forecasts are f...

  12. Probabilistic wind power forecasting with online model selection and warped gaussian process

    International Nuclear Information System (INIS)

    Kou, Peng; Liang, Deliang; Gao, Feng; Gao, Lin

    2014-01-01

    Highlights: • A new online ensemble model for the probabilistic wind power forecasting. • Quantifying the non-Gaussian uncertainties in wind power. • Online model selection that tracks the time-varying characteristic of wind generation. • Dynamically altering the input features. • Recursive update of base models. - Abstract: Based on the online model selection and the warped Gaussian process (WGP), this paper presents an ensemble model for the probabilistic wind power forecasting. This model provides the non-Gaussian predictive distributions, which quantify the non-Gaussian uncertainties associated with wind power. In order to follow the time-varying characteristics of wind generation, multiple time dependent base forecasting models and an online model selection strategy are established, thus adaptively selecting the most probable base model for each prediction. WGP is employed as the base model, which handles the non-Gaussian uncertainties in wind power series. Furthermore, a regime switch strategy is designed to modify the input feature set dynamically, thereby enhancing the adaptiveness of the model. In an online learning framework, the base models should also be time adaptive. To achieve this, a recursive algorithm is introduced, thus permitting the online updating of WGP base models. The proposed model has been tested on the actual data collected from both single and aggregated wind farms

  13. Enhancing Community Based Early Warning Systems in Nepal with Flood Forecasting Using Local and Global Models

    Science.gov (United States)

    Dugar, Sumit; Smith, Paul; Parajuli, Binod; Khanal, Sonu; Brown, Sarah; Gautam, Dilip; Bhandari, Dinanath; Gurung, Gehendra; Shakya, Puja; Kharbuja, RamGopal; Uprety, Madhab

    2017-04-01

    Operationalising effective Flood Early Warning Systems (EWS) in developing countries like Nepal poses numerous challenges, with complex topography and geology, sparse network of river and rainfall gauging stations and diverse socio-economic conditions. Despite these challenges, simple real-time monitoring based EWSs have been in place for the past decade. A key constraint of these simple systems is the very limited lead time for response - as little as 2-3 hours, especially for rivers originating from steep mountainous catchments. Efforts to increase lead time for early warning are focusing on imbedding forecasts into the existing early warning systems. In 2016, the Nepal Department of Hydrology and Meteorology (DHM) piloted an operational Probabilistic Flood Forecasting Model in major river basins across Nepal. This comprised a low data approach to forecast water levels, developed jointly through a research/practitioner partnership with Lancaster University and WaterNumbers (UK) and the International NGO Practical Action. Using Data-Based Mechanistic Modelling (DBM) techniques, the model assimilated rainfall and water levels to generate localised hourly flood predictions, which are presented as probabilistic forecasts, increasing lead times from 2-3 hours to 7-8 hours. The Nepal DHM has simultaneously started utilizing forecasts from the Global Flood Awareness System (GLoFAS) that provides streamflow predictions at the global scale based upon distributed hydrological simulations using numerical ensemble weather forecasts from the ECMWF (European Centre for Medium-Range Weather Forecasts). The aforementioned global and local models have already affected the approach to early warning in Nepal, being operational during the 2016 monsoon in the West Rapti basin in Western Nepal. On 24 July 2016, GLoFAS hydrological forecasts for the West Rapti indicated a sharp rise in river discharge above 1500 m3/sec (equivalent to the river warning level at 5 meters) with 53

  14. 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...

  15. Modeling and Forecasting Mortality With Economic Growth: A Multipopulation Approach.

    Science.gov (United States)

    Boonen, Tim J; Li, Hong

    2017-10-01

    Research on mortality modeling of multiple populations focuses mainly on extrapolating past mortality trends and summarizing these trends by one or more common latent factors. This article proposes a multipopulation stochastic mortality model that uses the explanatory power of economic growth. In particular, we extend the Li and Lee model (Li and Lee 2005) by including economic growth, represented by the real gross domestic product (GDP) per capita, to capture the common mortality trend for a group of populations with similar socioeconomic conditions. We find that our proposed model provides a better in-sample fit and an out-of-sample forecast performance. Moreover, it generates lower (higher) forecasted period life expectancy for countries with high (low) GDP per capita than the Li and Lee model.

  16. Regional forecasting with global atmospheric models

    International Nuclear Information System (INIS)

    Crowley, T.J.; North, G.R.; Smith, N.R.

    1994-05-01

    The scope of the report is to present the results of the fourth year's work on the atmospheric modeling part of the global climate studies task. The development testing of computer models and initial results are discussed. The appendices contain studies that provide supporting information and guidance to the modeling work and further details on computer model development. Complete documentation of the models, including user information, will be prepared under separate reports and manuals

  17. Modeling and forecasting of electrical power demands for capacity planning

    International Nuclear Information System (INIS)

    Al-Shobaki, S.; Mohsen, M.

    2007-01-01

    Jordan imports oil from neighboring countries for use in power production. As such, the cost of electricity production is high compared to oil producing countries. It is anticipated that Jordan will face major challenges in trying to meet the growing energy and electricity demands while also developing the energy sector in a way that reduces any adverse impacts on the economy, the environment and social life. This paper described the development of forecasting models to predict future generation and sales loads of electrical power in Jordan. Two models that could be used for the prediction of electrical energy demand in Amman, Jordan were developed and validated. An analysis of the data was also presented. The first model was based on the levels of energy generated by the National Electric Power Company (NEPCO) and the other was based on the levels of energy sold by the company in the same area. The models were compared and the percent error was presented. Energy demand was also forecasted across the next 60 months for both models. Results were then compared with the output of the in-house forecast model used by NEPCO to predict the levels of generated energy needed across the 60 months time period. It was concluded that the NEPCO model predicted energy demand higher than the validated generated data model by an average of 5.25 per cent. 8 refs., 5 tabs., 15 figs

  18. Modeling and forecasting of electrical power demands for capacity planning

    Energy Technology Data Exchange (ETDEWEB)

    Al-Shobaki, S. [Hashemite Univ., Zarka (Jordan). Dept. of Industrial Engineering; Mohsen, M. [Hashemite Univ., Zarka (Jordan). Dept. of Mechanical Engineering

    2007-07-01

    Jordan imports oil from neighboring countries for use in power production. As such, the cost of electricity production is high compared to oil producing countries. It is anticipated that Jordan will face major challenges in trying to meet the growing energy and electricity demands while also developing the energy sector in a way that reduces any adverse impacts on the economy, the environment and social life. This paper described the development of forecasting models to predict future generation and sales loads of electrical power in Jordan. Two models that could be used for the prediction of electrical energy demand in Amman, Jordan were developed and validated. An analysis of the data was also presented. The first model was based on the levels of energy generated by the National Electric Power Company (NEPCO) and the other was based on the levels of energy sold by the company in the same area. The models were compared and the percent error was presented. Energy demand was also forecasted across the next 60 months for both models. Results were then compared with the output of the in-house forecast model used by NEPCO to predict the levels of generated energy needed across the 60 months time period. It was concluded that the NEPCO model predicted energy demand higher than the validated generated data model by an average of 5.25 per cent. 8 refs., 5 tabs., 15 figs.

  19. Forecasting Lightning Threat using Cloud-resolving Model Simulations

    Science.gov (United States)

    McCaul, E. W., Jr.; Goodman, S. J.; LaCasse, K. M.; Cecil, D. J.

    2009-01-01

    As numerical forecasts capable of resolving individual convective clouds become more common, it is of interest to see if quantitative forecasts of lightning flash rate density are possible, based on fields computed by the numerical model. Previous observational research has shown robust relationships between observed lightning flash rates and inferred updraft and large precipitation ice fields in the mixed phase regions of storms, and that these relationships might allow simulated fields to serve as proxies for lightning flash rate density. It is shown in this paper that two simple proxy fields do indeed provide reasonable and cost-effective bases for creating time-evolving maps of predicted lightning flash rate density, judging from a series of diverse simulation case study events in North Alabama for which Lightning Mapping Array data provide ground truth. One method is based on the product of upward velocity and the mixing ratio of precipitating ice hydrometeors, modeled as graupel only, in the mixed phase region of storms at the -15\\dgc\\ level, while the second method is based on the vertically integrated amounts of ice hydrometeors in each model grid column. Each method can be calibrated by comparing domainwide statistics of the peak values of simulated flash rate proxy fields against domainwide peak total lightning flash rate density data from observations. Tests show that the first method is able to capture much of the temporal variability of the lightning threat, while the second method does a better job of depicting the areal coverage of the threat. A blended solution is designed to retain most of the temporal sensitivity of the first method, while adding the improved spatial coverage of the second. Weather Research and Forecast Model simulations of selected North Alabama cases show that this model can distinguish the general character and intensity of most convective events, and that the proposed methods show promise as a means of generating

  20. Forecasting wind-driven wildfires using an inverse modelling approach

    Directory of Open Access Journals (Sweden)

    O. Rios

    2014-06-01

    Full Text Available A technology able to rapidly forecast wildfire dynamics would lead to a paradigm shift in the response to emergencies, providing the Fire Service with essential information about the ongoing fire. This paper presents and explores a novel methodology to forecast wildfire dynamics in wind-driven conditions, using real-time data assimilation and inverse modelling. The forecasting algorithm combines Rothermel's rate of spread theory with a perimeter expansion model based on Huygens principle and solves the optimisation problem with a tangent linear approach and forward automatic differentiation. Its potential is investigated using synthetic data and evaluated in different wildfire scenarios. The results show the capacity of the method to quickly predict the location of the fire front with a positive lead time (ahead of the event in the order of 10 min for a spatial scale of 100 m. The greatest strengths of our method are lightness, speed and flexibility. We specifically tailor the forecast to be efficient and computationally cheap so it can be used in mobile systems for field deployment and operativeness. Thus, we put emphasis on producing a positive lead time and the means to maximise it.

  1. 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.

  2. Short-Term Power Plant GHG Emissions Forecasting Model

    International Nuclear Information System (INIS)

    Vidovic, D.

    2016-01-01

    In 2010, the share of greenhouse gas (GHG) emissions from power generation in the total emissions at the global level was about 25 percent. From January 1st, 2013 Croatian facilities have been involved in the European Union Emissions Trading System (EU ETS). The share of the ETS sector in total GHG emissions in Croatia in 2012 was about 30 percent, where power plants and heat generation facilities contributed to almost 50 percent. Since 2013 power plants are obliged to purchase all emission allowances. The paper describes the short-term climate forecasting model of greenhouse gas emissions from power plants while covering the daily load diagram of the system. Forecasting is done on an hourly domain typically for one day, it is possible and more days ahead. Forecasting GHG emissions in this way would enable power plant operators to purchase additional or sell surplus allowances on the market at the time. Example that describes the operation of the above mentioned forecasting model is given at the end of the paper.(author).

  3. Comparison of ensemble post-processing approaches, based on empirical and dynamical error modelisation of rainfall-runoff model forecasts

    Science.gov (United States)

    Chardon, J.; Mathevet, T.; Le Lay, M.; Gailhard, J.

    2012-04-01

    dynamic and processes, i. e. sample heterogeneity. For a same streamflow range corresponds different processes such as rising limbs or recession, where uncertainties are different. The dynamical approach improves reliability, skills and sharpness of forecasts and globally reduces confidence intervals width. When compared in details, the dynamical approach allows a noticeable reduction of confidence intervals during recessions where uncertainty is relatively lower and a slight increase of confidence intervals during rising limbs or snowmelt where uncertainty is greater. The dynamic approach, validated by forecaster's experience that considered the empirical approach not discriminative enough, improved forecaster's confidence and communication of uncertainties. Montanari, A. and Brath, A., (2004). A stochastic approach for assessing the uncertainty of rainfall-runoff simulations. Water Resources Research, 40, W01106, doi:10.1029/2003WR002540. Schaefli, B., Balin Talamba, D. and Musy, A., (2007). Quantifying hydrological modeling errors through a mixture of normal distributions. Journal of Hydrology, 332, 303-315.

  4. A state-dependent model for inflation forecasting

    OpenAIRE

    Andrea Stella; James H. Stock

    2012-01-01

    We develop a parsimonious bivariate model of inflation and unemployment that allows for persistent variation in trend inflation and the NAIRU. The model, which consists of five unobserved components (including the trends) with stochastic volatility, implies a time-varying VAR for changes in the rates of inflation and unemployment. The implied backwards-looking Phillips curve has a time-varying slope that is steeper in the 1970s than in the 1990s. Pseudo out-of-sample forecasting experiments i...

  5. COMBINING LONG MEMORY AND NONLINEAR MODEL OUTPUTS FOR INFLATION FORECAST

    OpenAIRE

    Heri Kuswanto; Irhamah Alimuhajin; Laylia Afidah

    2014-01-01

    Long memory and nonlinearity have been proven as two models that are easily to be mistaken. In other words, nonlinearity is a strong candidate of spurious long memory by introducing a certain degree of fractional integration that lies in the region of long memory. Indeed, nonlinear process belongs to short memory with zero integration order. The idea of the forecast is to obtain the future condition with minimum error. Some researches argued that no matter what the model is, the important thi...

  6. Results of verification and investigation of wind velocity field forecast. Verification of wind velocity field forecast model

    International Nuclear Information System (INIS)

    Ogawa, Takeshi; Kayano, Mitsunaga; Kikuchi, Hideo; Abe, Takeo; Saga, Kyoji

    1995-01-01

    In Environmental Radioactivity Research Institute, the verification and investigation of the wind velocity field forecast model 'EXPRESS-1' have been carried out since 1991. In fiscal year 1994, as the general analysis, the validity of weather observation data, the local features of wind field, and the validity of the positions of monitoring stations were investigated. The EXPRESS which adopted 500 m mesh so far was improved to 250 m mesh, and the heightening of forecast accuracy was examined, and the comparison with another wind velocity field forecast model 'SPEEDI' was carried out. As the results, there are the places where the correlation with other points of measurement is high and low, and it was found that for the forecast of wind velocity field, by excluding the data of the points with low correlation or installing simplified observation stations to take their data in, the forecast accuracy is improved. The outline of the investigation, the general analysis of weather observation data and the improvements of wind velocity field forecast model and forecast accuracy are reported. (K.I.)

  7. New Models for Forecasting Enrollments: Fuzzy Time Series and Neural Network Approaches.

    Science.gov (United States)

    Song, Qiang; Chissom, Brad S.

    Since university enrollment forecasting is very important, many different methods and models have been proposed by researchers. Two new methods for enrollment forecasting are introduced: (1) the fuzzy time series model; and (2) the artificial neural networks model. Fuzzy time series has been proposed to deal with forecasting problems within a…

  8. Performance Analysis of a Forecasting Relocation Model for One-Way Carsharing

    Directory of Open Access Journals (Sweden)

    Ganjar Alfian

    2017-06-01

    Full Text Available A carsharing service can be seen as a transport alternative between private and public transport that enables a group of people to share vehicles based at certain stations. The advanced carsharing service, one-way carsharing, enables customers to return the car to another station. However, one-way implementation generates an imbalanced distribution of cars in each station. Thus, this paper proposes forecasting relocation to solve car distribution imbalances for one-way carsharing services. A discrete event simulation model was developed to help evaluate the proposed model performance. A real case dataset was used to find the best simulation result. The results provide a clear insight into the impact of forecasting relocation on high system utilization and the reservation acceptance ratio compared to traditional relocation methods.

  9. Evaluating uncertainty estimates in hydrologic models: borrowing measures from the forecast verification community

    Directory of Open Access Journals (Sweden)

    K. J. Franz

    2011-11-01

    Full Text Available The hydrologic community is generally moving towards the use of probabilistic estimates of streamflow, primarily through the implementation of Ensemble Streamflow Prediction (ESP systems, ensemble data assimilation methods, or multi-modeling platforms. However, evaluation of probabilistic outputs has not necessarily kept pace with ensemble generation. Much of the modeling community is still performing model evaluation using standard deterministic measures, such as error, correlation, or bias, typically applied to the ensemble mean or median. Probabilistic forecast verification methods have been well developed, particularly in the atmospheric sciences, yet few have been adopted for evaluating uncertainty estimates in hydrologic model simulations. In the current paper, we overview existing probabilistic forecast verification methods and apply the methods to evaluate and compare model ensembles produced from two different parameter uncertainty estimation methods: the Generalized Uncertainty Likelihood Estimator (GLUE, and the Shuffle Complex Evolution Metropolis (SCEM. Model ensembles are generated for the National Weather Service SACramento Soil Moisture Accounting (SAC-SMA model for 12 forecast basins located in the Southeastern United States. We evaluate the model ensembles using relevant metrics in the following categories: distribution, correlation, accuracy, conditional statistics, and categorical statistics. We show that the presented probabilistic metrics are easily adapted to model simulation ensembles and provide a robust analysis of model performance associated with parameter uncertainty. Application of these methods requires no information in addition to what is already available as part of traditional model validation methodology and considers the entire ensemble or uncertainty range in the approach.

  10. Model Uncertainty and Exchange Rate Forecasting

    NARCIS (Netherlands)

    Kouwenberg, R.; Markiewicz, A.; Verhoeks, R.; Zwinkels, R.C.J.

    2017-01-01

    Exchange rate models with uncertain and incomplete information predict that investors focus on a small set of fundamentals that changes frequently over time. We design a model selection rule that captures the current set of fundamentals that best predicts the exchange rate. Out-of-sample tests show

  11. A forecast comparison of volatility models

    DEFF Research Database (Denmark)

    Hansen, Peter Reinhard; Lunde, Asger

    2005-01-01

    We compare 330 ARCH-type models in terms of their ability to describe the conditional variance. The models are compared out-of-sample using DM-$ exchange rate data and IBM return data, where the latter is based on a new data set of realized variance. We find no evidence that a GARCH(1,1) is outpe......We compare 330 ARCH-type models in terms of their ability to describe the conditional variance. The models are compared out-of-sample using DM-$ exchange rate data and IBM return data, where the latter is based on a new data set of realized variance. We find no evidence that a GARCH(1...... snooping (RC). Our empirical results show that the RC lacks power to an extent that makes it unable to distinguish "good" and "bad" models in our analysis....

  12. Regional forecasting with global atmospheric models

    International Nuclear Information System (INIS)

    Crowley, T.J.; North, G.R.; Smith, N.R.

    1994-05-01

    This report was prepared by the Applied Research Corporation (ARC), College Station, Texas, under subcontract to Pacific Northwest Laboratory (PNL) as part of a global climate studies task. The task supports site characterization work required for the selection of a potential high-level nuclear waste repository and is part of the Performance Assessment Scientific Support (PASS) Program at PNL. The work is under the overall direction of the Office of Civilian Radioactive Waste Management (OCRWM), US Department of Energy Headquarters, Washington, DC. The scope of the report is to present the results of the third year's work on the atmospheric modeling part of the global climate studies task. The development testing of computer models and initial results are discussed. The appendices contain several studies that provide supporting information and guidance to the modeling work and further details on computer model development. Complete documentation of the models, including user information, will be prepared under separate reports and manuals

  13. Operational hydrological forecasting in Bavaria. Part II: Ensemble forecasting

    Science.gov (United States)

    Ehret, U.; Vogelbacher, A.; Moritz, K.; Laurent, S.; Meyer, I.; Haag, I.

    2009-04-01

    In part I of this study, the operational flood forecasting system in Bavaria and an approach to identify and quantify forecast uncertainty was introduced. The approach is split into the calculation of an empirical 'overall error' from archived forecasts and the calculation of an empirical 'model error' based on hydrometeorological forecast tests, where rainfall observations were used instead of forecasts. The 'model error' can especially in upstream catchments where forecast uncertainty is strongly dependent on the current predictability of the atrmosphere be superimposed on the spread of a hydrometeorological ensemble forecast. In Bavaria, two meteorological ensemble prediction systems are currently tested for operational use: the 16-member COSMO-LEPS forecast and a poor man's ensemble composed of DWD GME, DWD Cosmo-EU, NCEP GFS, Aladin-Austria, MeteoSwiss Cosmo-7. The determination of the overall forecast uncertainty is dependent on the catchment characteristics: 1. Upstream catchment with high influence of weather forecast a) A hydrological ensemble forecast is calculated using each of the meteorological forecast members as forcing. b) Corresponding to the characteristics of the meteorological ensemble forecast, each resulting forecast hydrograph can be regarded as equally likely. c) The 'model error' distribution, with parameters dependent on hydrological case and lead time, is added to each forecast timestep of each ensemble member d) For each forecast timestep, the overall (i.e. over all 'model error' distribution of each ensemble member) error distribution is calculated e) From this distribution, the uncertainty range on a desired level (here: the 10% and 90% percentile) is extracted and drawn as forecast envelope. f) As the mean or median of an ensemble forecast does not necessarily exhibit meteorologically sound temporal evolution, a single hydrological forecast termed 'lead forecast' is chosen and shown in addition to the uncertainty bounds. This can be

  14. Modelling and forecasting monthly swordfish catches in the Eastern Mediterranean

    Directory of Open Access Journals (Sweden)

    Konstantinos I. Stergiou

    2003-04-01

    Full Text Available In this study, we used the X-11 census technique for modelling and forecasting the monthly swordfish (Xiphias gladius catches in the Greek Seas during 1982-1996 and 1997 respectively, using catches reported by the National Statistical Service of Greece (NSSG. Forecasts built with X-11 were also compared with those derived from ARIMA andWinter’s exponential smoothing (WES models. The X-11 method captured the features of the study series and outperformed the other two methods, in terms of both fitting and forecasting performance, for all the accuracy measures used. Thus, with the exception of October, November and December 1997, when the corresponding absolute percentage error(APE values were very high (as high as 178.6% because of the low level of the catches, monthly catches during the remaining months of 1997 were predicted accurately, with a mean APE of 12.5%. In contrast, the mean APE values of the other two methods for the same months were higher (ARIMA: 14.6%; WES: 16.6%. The overall good performance of X-11 andthe fact that it provides an insight into the various components (i.e. the seasonal, trend-cycle and irregular components of the time series of interest justify its use in fisheries research. The basic features of the swordfish catches revealed by the application of the X-11 method, the effect of the length of the forecasting horizon on forecasting accuracy and the accuracy of the catches reported by NSSG are also discussed.

  15. 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.

  16. 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.

  17. Forecasting models for national economic planning

    CERN Document Server

    Heesterman, A R G

    1972-01-01

    This book is about the specification of linear econometric models, and for this reason some important related fields have been deliberately omitted. I did not want to discuss the problems of parameter-estimation, at least not in any detail, as there are other books on these problems written by specialized statisticians. This book is about the models them­ selves and macro-economic models in particular. A second related sub­ ject is the policy decision that can be made with the help of a model. While I did write a chapter on policy decisions, I limited myself to some extent because of my views on planning as such. The logical approach to this problem is in terms of mathematical programming, but our models and our ideas about the policies we want are too crude for its effective utilisation. A realistic formulation of the problem should involve non­ linearities in an essential way, the models I consider (and most existing models) are linear. At the present state of econometrics, I do not really believe in suc...

  18. A national framework for flood forecasting model assessment for use in operations and investment planning over England and Wales

    Science.gov (United States)

    Moore, Robert J.; Wells, Steven C.; Cole, Steven J.

    2016-04-01

    It has been common for flood forecasting systems to be commissioned at a catchment or regional level in response to local priorities and hydrological conditions, leading to variety in system design and model choice. As systems mature and efficiencies of national management are sought, there can be a drive towards system rationalisation, gaining an overview of model performance and consideration of simplification through model-type convergence. Flood forecasting model assessments, whilst overseen at a national level, may be commissioned and managed at a catchment and regional level, take a variety of forms and be large in number. This presents a challenge when an integrated national assessment is required to guide operational use of flood forecasts and plan future investment in flood forecasting models and supporting hydrometric monitoring. This contribution reports on how a nationally consistent framework for flood forecasting model performance has been developed to embrace many past, ongoing and future assessments for local river systems by engineering consultants across England & Wales. The outcome is a Performance Summary for every site model assessed which, on a single page, contains relevant catchment information for context, a selection of overlain forecast and observed hydrographs and a set of performance statistics with associated displays of novel condensed form. One display provides performance comparison with other models that may exist for the site. The performance statistics include skill scores for forecasting events (flow/level threshold crossings) of differing severity/rarity, indicating their probability and likely timing, which have real value in an operational setting. The local models assessed can be of any type and span rainfall-runoff (conceptual and transfer function) and flow routing (hydrological and hydrodynamic) forms. Also accommodated by the framework is the national G2G (Grid-to-Grid) distributed hydrological model, providing area

  19. Daily reservoir inflow forecasting combining QPF into ANNs model

    Science.gov (United States)

    Zhang, Jun; Cheng, Chun-Tian; Liao, Sheng-Li; Wu, Xin-Yu; Shen, Jian-Jian

    2009-01-01

    Daily reservoir inflow predictions with lead-times of several days are essential to the operational planning and scheduling of hydroelectric power system. The demand for quantitative precipitation forecasting (QPF) is increasing in hydropower operation with the dramatic advances in the numerical weather prediction (NWP) models. This paper presents a simple and an effective algorithm for daily reservoir inflow predictions which solicits the observed precipitation, forecasted precipitation from QPF as predictors and discharges in following 1 to 6 days as predicted targets for multilayer perceptron artificial neural networks (MLP-ANNs) modeling. An improved error back-propagation algorithm with self-adaptive learning rate and self-adaptive momentum coefficient is used to make the supervised training procedure more efficient in both time saving and search optimization. Several commonly used error measures are employed to evaluate the performance of the proposed model and the results, compared with that of ARIMA model, show that the proposed model is capable of obtaining satisfactory forecasting not only in goodness of fit but also in generalization. Furthermore, the presented algorithm is integrated into a practical software system which has been severed for daily inflow predictions with lead-times varying from 1 to 6 days of more than twenty reservoirs operated by the Fujian Province Grid Company, China.

  20. Estimating and Forecasting Generalized Fractional Long Memory Stochastic Volatility Models

    Directory of Open Access Journals (Sweden)

    Shelton Peiris

    2017-12-01

    Full Text Available This paper considers a flexible class of time series models generated by Gegenbauer polynomials incorporating the long memory in stochastic volatility (SV components in order to develop the General Long Memory SV (GLMSV model. We examine the corresponding statistical properties of this model, discuss the spectral likelihood estimation and investigate the finite sample properties via Monte Carlo experiments. We provide empirical evidence by applying the GLMSV model to three exchange rate return series and conjecture that the results of out-of-sample forecasts adequately confirm the use of GLMSV model in certain financial applications.

  1. 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.

  2. Scaling forecast models for wind turbulence and wind turbine power intermittency

    Science.gov (United States)

    Duran Medina, Olmo; Schmitt, Francois G.; Calif, Rudy

    2017-04-01

    The intermittency of the wind turbine power remains an important issue for the massive development of this renewable energy. The energy peaks injected in the electric grid produce difficulties in the energy distribution management. Hence, a correct forecast of the wind power in the short and middle term is needed due to the high unpredictability of the intermittency phenomenon. We consider a statistical approach through the analysis and characterization of stochastic fluctuations. The theoretical framework is the multifractal modelisation of wind velocity fluctuations. Here, we consider three wind turbine data where two possess a direct drive technology. Those turbines are producing energy in real exploitation conditions and allow to test our forecast models of power production at a different time horizons. Two forecast models were developed based on two physical principles observed in the wind and the power time series: the scaling properties on the one hand and the intermittency in the wind power increments on the other. The first tool is related to the intermittency through a multifractal lognormal fit of the power fluctuations. The second tool is based on an analogy of the power scaling properties with a fractional brownian motion. Indeed, an inner long-term memory is found in both time series. Both models show encouraging results since a correct tendency of the signal is respected over different time scales. Those tools are first steps to a search of efficient forecasting approaches for grid adaptation facing the wind energy fluctuations.

  3. Smart Irrigation From Soil Moisture Forecast Using Satellite And Hydro -Meteorological Modelling

    Science.gov (United States)

    Corbari, Chiara; Mancini, Marco; Ravazzani, Giovanni; Ceppi, Alessandro; Salerno, Raffaele; Sobrino, Josè

    2017-04-01

    Increased water demand and climate change impacts have recently enhanced the need to improve water resources management, even in those areas which traditionally have an abundant supply of water. The highest consumption of water is devoted to irrigation for agricultural production, and so it is in this area that efforts have to be focused to study possible interventions. The SIM project funded by EU in the framework of the WaterWorks2014 - Water Joint Programming Initiative aims at developing an operational tool for real-time forecast of crops irrigation water requirements to support parsimonious water management and to optimize irrigation scheduling providing real-time and forecasted soil moisture behavior at high spatial and temporal resolutions with forecast horizons from few up to thirty days. This study discusses advances in coupling satellite driven soil water balance model and meteorological forecast as support for precision irrigation use comparing different case studies in Italy, in the Netherlands, in China and Spain, characterized by different climatic conditions, water availability, crop types and irrigation techniques and water distribution rules. Herein, the applications in two operative farms in vegetables production in the South of Italy where semi-arid climatic conditions holds, two maize fields in Northern Italy in a more water reach environment with flood irrigation will be presented. This system combines state of the art mathematical models and new technologies for environmental monitoring, merging ground observed data with Earth observations. Discussion on the methodology approach is presented, comparing for a reanalysis periods the forecast system outputs with observed soil moisture and crop water needs proving the reliability of the forecasting system and its benefits. The real-time visualization of the implemented system is also presented through web-dashboards.

  4. Modeling and forecasting natural gas demand in Bangladesh

    International Nuclear Information System (INIS)

    Wadud, Zia; Dey, Himadri S.; Kabir, Md. Ashfanoor; Khan, Shahidul I.

    2011-01-01

    Natural gas is the major indigenous source of energy in Bangladesh and accounts for almost one-half of all primary energy used in the country. Per capita and total energy use in Bangladesh is still very small, and it is important to understand how energy, and natural gas demand will evolve in the future. We develop a dynamic econometric model to understand the natural gas demand in Bangladesh, both in the national level, and also for a few sub-sectors. Our demand model shows large long run income elasticity - around 1.5 - for aggregate demand for natural gas. Forecasts into the future also show a larger demand in the future than predicted by various national and multilateral organizations. Even then, it is possible that our forecasts could still be at the lower end of the future energy demand. Price response was statistically not different from zero, indicating that prices are possibly too low and that there is a large suppressed demand for natural gas in the country. - Highlights: → Natural gas demand is modeled using dynamic econometric methods, first of its kind in Bangladesh. → Income elasticity for aggregate natural gas demand in Bangladesh is large-around 1.5. → Demand is price insensitive, indicating too low prices and/or presence of large suppressed demand. → Demand forecasts reveal large divergence from previous estimates, which is important for planning. → Attempts to model demand for end-use sectors were successful only for the industrial sector.

  5. An efficient and simplified model for forecasting using SRM

    International Nuclear Information System (INIS)

    Asif, H.M.; Hyat, M.F.; Ahmad, T.

    2014-01-01

    Learning form continuous financial systems play a vital role in enterprise operations. One of the most sophisticated non-parametric supervised learning classifiers, SVM (Support Vector Machines), provides robust and accurate results, however it may require intense computation and other resources. The heart of SLT (Statistical Learning Theory), SRM (Structural Risk Minimization )Principle can also be used for model selection. In this paper, we focus on comparing the performance of model estimation using SRM with SVR (Support Vector Regression) for forecasting the retail sales of consumer products. The potential benefits of an accurate sales forecasting technique in businesses are immense. Retail sales forecasting is an integral part of strategic business planning in areas such as sales planning, marketing research, pricing, production planning and scheduling. Performance comparison of support vector regression with model selection using SRM shows comparable results to SVR but in a computationally efficient manner. This research targeted the real life data to conclude the results after investigating the computer generated datasets for different types of model building. (author)

  6. Electricity prices forecasting by automatic dynamic harmonic regression models

    International Nuclear Information System (INIS)

    Pedregal, Diego J.; Trapero, Juan R.

    2007-01-01

    The changes experienced by electricity markets in recent years have created the necessity for more accurate forecast tools of electricity prices, both for producers and consumers. Many methodologies have been applied to this aim, but in the view of the authors, state space models are not yet fully exploited. The present paper proposes a univariate dynamic harmonic regression model set up in a state space framework for forecasting prices in these markets. The advantages of the approach are threefold. Firstly, a fast automatic identification and estimation procedure is proposed based on the frequency domain. Secondly, the recursive algorithms applied offer adaptive predictions that compare favourably with respect to other techniques. Finally, since the method is based on unobserved components models, explicit information about trend, seasonal and irregular behaviours of the series can be extracted. This information is of great value to the electricity companies' managers in order to improve their strategies, i.e. it provides management innovations. The good forecast performance and the rapid adaptability of the model to changes in the data are illustrated with actual prices taken from the PJM interconnection in the US and for the Spanish market for the year 2002. (author)

  7. Modeling and forecasting natural gas demand in Bangladesh

    Energy Technology Data Exchange (ETDEWEB)

    Wadud, Zia, E-mail: ziawadud@yahoo.com [Bangladesh University of Engineering and Technology (Bangladesh); Dey, Himadri S. [University of Notre Dame (United States); Kabir, Md. Ashfanoor; Khan, Shahidul I. [Bangladesh University of Engineering and Technology (Bangladesh)

    2011-11-15

    Natural gas is the major indigenous source of energy in Bangladesh and accounts for almost one-half of all primary energy used in the country. Per capita and total energy use in Bangladesh is still very small, and it is important to understand how energy, and natural gas demand will evolve in the future. We develop a dynamic econometric model to understand the natural gas demand in Bangladesh, both in the national level, and also for a few sub-sectors. Our demand model shows large long run income elasticity - around 1.5 - for aggregate demand for natural gas. Forecasts into the future also show a larger demand in the future than predicted by various national and multilateral organizations. Even then, it is possible that our forecasts could still be at the lower end of the future energy demand. Price response was statistically not different from zero, indicating that prices are possibly too low and that there is a large suppressed demand for natural gas in the country. - Highlights: > Natural gas demand is modeled using dynamic econometric methods, first of its kind in Bangladesh. > Income elasticity for aggregate natural gas demand in Bangladesh is large-around 1.5. > Demand is price insensitive, indicating too low prices and/or presence of large suppressed demand. > Demand forecasts reveal large divergence from previous estimates, which is important for planning. > Attempts to model demand for end-use sectors were successful only for the industrial sector.

  8. An Efficient and Simplified Model for Forecasting using SRM

    Directory of Open Access Journals (Sweden)

    Hafiz Muhammad Shahzad Asif

    2014-01-01

    Full Text Available Learning form continuous financial systems play a vital role in enterprise operations. One of the most sophisticated non-parametric supervised learning classifiers, SVM (Support Vector Machines, provides robust and accurate results, however it may require intense computation and other resources. The heart of SLT (Statistical Learning Theory, SRM (Structural Risk Minimization Principle can also be used for model selection. In this paper, we focus on comparing the performance of model estimation using SRM with SVR (Support Vector Regression for forecasting the retail sales of consumer products. The potential benefits of an accurate sales forecasting technique in businesses are immense. Retail sales forecasting is an integral part of strategic business planning in areas such as sales planning, marketing research, pricing, production planning and scheduling. Performance comparison of support vector regression with model selection using SRM shows comparable results to SVR but in a computationally efficient manner. This research targeted the real life data to conclude the results after investigating the computer generated datasets for different types of model building

  9. Forecasting characteristic earthquakes in a minimalist model

    DEFF Research Database (Denmark)

    Vázquez-Prada, M.; Pacheco, A.; González, Á.

    2003-01-01

    -dimensional numerical exploration of the loss function. This first strategy is then refined by considering a classification of the seismic cycles of the model according to the presence, or not, of some factors related to the seismicity observed in the cycle. These factors, statistically speaking, enlarge or shorten...

  10. MODELLING CHALLENGES TO FORECAST URBAN GOODS DEMAND FOR RAIL

    Directory of Open Access Journals (Sweden)

    Antonio COMI

    2015-12-01

    Full Text Available This paper explores the new research challenges for forecasting urban goods demand by rail. In fact, the growing interest to find urban logistics solutions for improving city sustainability and liveability, mainly due to the reduction of urban road accessibility and environmental constraints, has pushed to explore solutions alternative to the road. Multimodal urban logistics, based on the use of railway, seem an interesting alternative solution, but it remained mainly at conceptual level. Few studies have explored the factors, that push actors to find competitive such a system with respect to the road, and modelling framework for forecasting the relative demand. Therefore, paper reviews the current literature, investigates the factors involved in choosing such a mode, and finally, recalls a recent modelling framework and hence proposes some advancements that allow to point out the rail transport alternative.

  11. Analysis and forecast of railway coal transportation volume based on BP neural network combined forecasting model

    Science.gov (United States)

    Xu, Yongbin; Xie, Haihong; Wu, Liuyi

    2018-05-01

    The share of coal transportation in the total railway freight volume is about 50%. As is widely acknowledged, coal industry is vulnerable to the economic situation and national policies. Coal transportation volume fluctuates significantly under the new economic normal. Grasp the overall development trend of railway coal transportation market, have important reference and guidance significance to the railway and coal industry decision-making. By analyzing the economic indicators and policy implications, this paper expounds the trend of the coal transportation volume, and further combines the economic indicators with the high correlation with the coal transportation volume with the traditional traffic prediction model to establish a combined forecasting model based on the back propagation neural network. The error of the prediction results is tested, which proves that the method has higher accuracy and has practical application.

  12. Probabilistic Forecast of Wind Power Generation by Stochastic Differential Equation Models

    KAUST Repository

    Elkantassi, Soumaya

    2017-01-01

    Reliable forecasting of wind power generation is crucial to optimal control of costs in generation of electricity with respect to the electricity demand. Here, we propose and analyze stochastic wind power forecast models described by parametrized

  13. Weather Research and Forecasting (WRF) Regional Atmospheric Model: Maui-Oahu

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the Hawaiian islands of Oahu,...

  14. Weather Research and Forecasting (WRF) Regional Atmospheric Model: Main Hawaiian Islands

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the Main Hawaiian Islands (MHI)...

  15. Recent advances in operational seasonal forecasting in South Africa: Models, infrastructure and networks

    CSIR Research Space (South Africa)

    Landman, WA

    2011-11-01

    Full Text Available The various institutions involved with seasonal forecast development and production are discussed. New modelling approaches and the establishment of infrastructures to improve forecast dissemination are discussed....

  16. Modeling and Forecasting Persistent Financial Durations

    Czech Academy of Sciences Publication Activity Database

    Žikeš, F.; Baruník, Jozef; Shenai, N.

    2017-01-01

    Roč. 36, č. 10 (2017), s. 1081-1110 ISSN 0747-4938 R&D Projects: GA ČR GA13-32263S EU Projects: European Commission 612955 - FINMAP Institutional support: RVO:67985556 Keywords : price durations * long memory * multifractal models * realized volatility * Whittle estimation Subject RIV: AH - Economics OBOR OECD: Applied Economics , Econometrics Impact factor: 1.333, year: 2016 http://library.utia.cas.cz/separaty/2014/E/barunik-0434201.pdf

  17. State-level electricity demand forecasting model. [For 1980, 1985, 1990

    Energy Technology Data Exchange (ETDEWEB)

    Nguyen, H. D.

    1978-01-01

    This note briefly describes the Oak Ridge National Laboratory (ORNL) state-level electricity demand (SLED) forecasting model developed for the Nuclear Regulatory Commission. Specifically, the note presents (1) the special features of the model, (2) the methodology used to forecast electricity demand, and (3) forecasts of electricity demand and average price by sector for 15 states for 1980, 1985, 1990.

  18. Local TEC Modelling and Forecasting using Neural Networks

    Science.gov (United States)

    Tebabal, A.; Radicella, S. M.; Nigussie, M.; Damtie, B.; Nava, B.; Yizengaw, E.

    2017-12-01

    Abstract Modelling the Earth's ionospheric characteristics is the focal task for the ionospheric community to mitigate its effect on the radio communication, satellite navigation and technologies. However, several aspects of modelling are still challenging, for example, the storm time characteristics. This paper presents modelling efforts of TEC taking into account solar and geomagnetic activity, time of the day and day of the year using neural networks (NNs) modelling technique. The NNs have been designed with GPS-TEC measured data from low and mid-latitude GPS stations. The training was conducted using the data obtained for the period from 2011 to 2014. The model prediction accuracy was evaluated using data of year 2015. The model results show that diurnal and seasonal trend of the GPS-TEC is well reproduced by the model for the two stations. The seasonal characteristics of GPS-TEC is compared with NN and NeQuick 2 models prediction when the latter one is driven by the monthly average value of solar flux. It is found that NN model performs better than the corresponding NeQuick 2 model for low latitude region. For the mid-latitude both NN and NeQuick 2 models reproduce the average characteristics of TEC variability quite successfully. An attempt of one day ahead forecast of TEC at the two locations has been made by introducing as driver previous day solar flux and geomagnetic index values. The results show that a reasonable day ahead forecast of local TEC can be achieved.

  19. Forecasting municipal solid waste generation using artificial intelligence modelling approaches.

    Science.gov (United States)

    Abbasi, Maryam; El Hanandeh, Ali

    2016-10-01

    Municipal solid waste (MSW) management is a major concern to local governments to protect human health, the environment and to preserve natural resources. The design and operation of an effective MSW management system requires accurate estimation of future waste generation quantities. The main objective of this study was to develop a model for accurate forecasting of MSW generation that helps waste related organizations to better design and operate effective MSW management systems. Four intelligent system algorithms including support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and k-nearest neighbours (kNN) were tested for their ability to predict monthly waste generation in the Logan City Council region in Queensland, Australia. Results showed artificial intelligence models have good prediction performance and could be successfully applied to establish municipal solid waste forecasting models. Using machine learning algorithms can reliably predict monthly MSW generation by training with waste generation time series. In addition, results suggest that ANFIS system produced the most accurate forecasts of the peaks while kNN was successful in predicting the monthly averages of waste quantities. Based on the results, the total annual MSW generated in Logan City will reach 9.4×10(7)kg by 2020 while the peak monthly waste will reach 9.37×10(6)kg. Copyright © 2016 Elsevier Ltd. All rights reserved.

  20. IMD's model for forecasting monsoon in India

    Digital Repository Service at National Institute of Oceanography (India)

    Varkey, M.J.

    feeling of the subject to the younger generation. There is an increa s ing need for modelling, simulating and optimizing technological, physical, medi cal and economic processes. In fa ct, recently a study 2 program ent i- tled ?Tech0... . This is the first instance an Indian scie n- tist has been elected as a foreign fellow. C. N. R. Rao is already a member of all the major academies of the world. MEETING REPORTS Nutrition is the key to health and nation?s develo p ment* So say...

  1. Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models

    International Nuclear Information System (INIS)

    Tan, Zhongfu; Zhang, Jinliang; Xu, Jun; Wang, Jianhui

    2010-01-01

    This paper proposes a novel price forecasting method based on wavelet transform combined with ARIMA and GARCH models. By wavelet transform, the historical price series is decomposed and reconstructed into one approximation series and some detail series. Then each subseries can be separately predicted by a suitable time series model. The final forecast is obtained by composing the forecasted results of each subseries. This proposed method is examined on Spanish and PJM electricity markets and compared with some other forecasting methods. (author)

  2. Artificial neural network models' application for radioactive substances' migration forecasting in soil

    International Nuclear Information System (INIS)

    Kovalenko, V.I.; Khil'ko, O.S.; Kundas, S.P.

    2009-01-01

    The work is indicated to the use of artificial neural network (ANN) models in program complex SPS for radioactive substances' migration forecasting in soil. For the problem solution two ANN models are used. One of them forecasts radioactive substances' migration, another carries out forecasting of physical and chemical soil properties. Program complex SPS allows to achieve a low error of forecasting (no more than 5 %) and high training speed. (authors)

  3. Forecasting macroeconomic variables using neural network models and three automated model selection techniques

    DEFF Research Database (Denmark)

    Kock, Anders Bredahl; Teräsvirta, Timo

    2016-01-01

    When forecasting with neural network models one faces several problems, all of which influence the accuracy of the forecasts. First, neural networks are often hard to estimate due to their highly nonlinear structure. To alleviate the problem, White (2006) presented a solution (QuickNet) that conv...

  4. Deterministic and heuristic models of forecasting spare parts demand

    Directory of Open Access Journals (Sweden)

    Ivan S. Milojević

    2012-04-01

    Full Text Available Knowing the demand of spare parts is the basis for successful spare parts inventory management. Inventory management has two aspects. The first one is operational management: acting according to certain models and making decisions in specific situations which could not have been foreseen or have not been encompassed by models. The second aspect is optimization of the model parameters by means of inventory management. Supply items demand (asset demand is the expression of customers' needs in units in the desired time and it is one of the most important parameters in the inventory management. The basic task of the supply system is demand fulfillment. In practice, demand is expressed through requisition or request. Given the conditions in which inventory management is considered, demand can be: - deterministic or stochastic, - stationary or nonstationary, - continuous or discrete, - satisfied or unsatisfied. The application of the maintenance concept is determined by the technological level of development of the assets being maintained. For example, it is hard to imagine that the concept of self-maintenance can be applied to assets developed and put into use 50 or 60 years ago. Even less complex concepts cannot be applied to those vehicles that only have indicators of engine temperature - those that react only when the engine is overheated. This means that the maintenance concepts that can be applied are the traditional preventive maintenance and the corrective maintenance. In order to be applied in a real system, modeling and simulation methods require a completely regulated system and that is not the case with this spare parts supply system. Therefore, this method, which also enables the model development, cannot be applied. Deterministic models of forecasting are almost exclusively related to the concept of preventive maintenance. Maintenance procedures are planned in advance, in accordance with exploitation and time resources. Since the timing

  5. Bounding species distribution models

    Directory of Open Access Journals (Sweden)

    Thomas J. STOHLGREN, Catherine S. JARNEVICH, Wayne E. ESAIAS,Jeffrey T. MORISETTE

    2011-10-01

    Full Text Available Species distribution models are increasing in popularity for mapping suitable habitat for species of management concern. Many investigators now recognize that extrapolations of these models with geographic information systems (GIS might be sensitive to the environmental bounds of the data used in their development, yet there is no recommended best practice for “clamping” model extrapolations. We relied on two commonly used modeling approaches: classification and regression tree (CART and maximum entropy (Maxent models, and we tested a simple alteration of the model extrapolations, bounding extrapolations to the maximum and minimum values of primary environmental predictors, to provide a more realistic map of suitable habitat of hybridized Africanized honey bees in the southwestern United States. Findings suggest that multiple models of bounding, and the most conservative bounding of species distribution models, like those presented here, should probably replace the unbounded or loosely bounded techniques currently used [Current Zoology 57 (5: 642–647, 2011].

  6. Bounding Species Distribution Models

    Science.gov (United States)

    Stohlgren, Thomas J.; Jarnevich, Cahterine S.; Morisette, Jeffrey T.; Esaias, Wayne E.

    2011-01-01

    Species distribution models are increasing in popularity for mapping suitable habitat for species of management concern. Many investigators now recognize that extrapolations of these models with geographic information systems (GIS) might be sensitive to the environmental bounds of the data used in their development, yet there is no recommended best practice for "clamping" model extrapolations. We relied on two commonly used modeling approaches: classification and regression tree (CART) and maximum entropy (Maxent) models, and we tested a simple alteration of the model extrapolations, bounding extrapolations to the maximum and minimum values of primary environmental predictors, to provide a more realistic map of suitable habitat of hybridized Africanized honey bees in the southwestern United States. Findings suggest that multiple models of bounding, and the most conservative bounding of species distribution models, like those presented here, should probably replace the unbounded or loosely bounded techniques currently used [Current Zoology 57 (5): 642-647, 2011].

  7. Artificial Neural Network for Short-Term Load Forecasting in Distribution Systems

    Directory of Open Access Journals (Sweden)

    Luis Hernández

    2014-03-01

    Full Text Available The new paradigms and latest developments in the Electrical Grid are based on the introduction of distributed intelligence at several stages of its physical layer, giving birth to concepts such as Smart Grids, Virtual Power Plants, microgrids, Smart Buildings and Smart Environments. Distributed Generation (DG is a philosophy in which energy is no longer produced exclusively in huge centralized plants, but also in smaller premises which take advantage of local conditions in order to minimize transmission losses and optimize production and consumption. This represents a new opportunity for renewable energy, because small elements such as solar panels and wind turbines are expected to be scattered along the grid, feeding local installations or selling energy to the grid depending on their local generation/consumption conditions. The introduction of these highly dynamic elements will lead to a substantial change in the curves of demanded energy. The aim of this paper is to apply Short-Term Load Forecasting (STLF in microgrid environments with curves and similar behaviours, using two different data sets: the first one packing electricity consumption information during four years and six months in a microgrid along with calendar data, while the second one will be just four months of the previous parameters along with the solar radiation from the site. For the first set of data different STLF models will be discussed, studying the effect of each variable, in order to identify the best one. That model will be employed with the second set of data, in order to make a comparison with a new model that takes into account the solar radiation, since the photovoltaic installations of the microgrid will cause the power demand to fluctuate depending on the solar radiation.

  8. Trend time-series modeling and forecasting with neural networks.

    Science.gov (United States)

    Qi, Min; Zhang, G Peter

    2008-05-01

    Despite its great importance, there has been no general consensus on how to model the trends in time-series data. Compared to traditional approaches, neural networks (NNs) have shown some promise in time-series forecasting. This paper investigates how to best model trend time series using NNs. Four different strategies (raw data, raw data with time index, detrending, and differencing) are used to model various trend patterns (linear, nonlinear, deterministic, stochastic, and breaking trend). We find that with NNs differencing often gives meritorious results regardless of the underlying data generating processes (DGPs). This finding is also confirmed by the real gross national product (GNP) series.

  9. Profit Forecast Model Using Monte Carlo Simulation in Excel

    Directory of Open Access Journals (Sweden)

    Petru BALOGH

    2014-01-01

    Full Text Available Profit forecast is very important for any company. The purpose of this study is to provide a method to estimate the profit and the probability of obtaining the expected profit. Monte Carlo methods are stochastic techniques–meaning they are based on the use of random numbers and probability statistics to investigate problems. Monte Carlo simulation furnishes the decision-maker with a range of possible outcomes and the probabilities they will occur for any choice of action. Our example of Monte Carlo simulation in Excel will be a simplified profit forecast model. Each step of the analysis will be described in detail. The input data for the case presented: the number of leads per month, the percentage of leads that result in sales, , the cost of a single lead, the profit per sale and fixed cost, allow obtaining profit and associated probabilities of achieving.

  10. Application of nonlinear forecasting techniques for meteorological modeling

    Directory of Open Access Journals (Sweden)

    V. Pérez-Muñuzuri

    2000-10-01

    Full Text Available A nonlinear forecasting method was used to predict the behavior of a cloud coverage time series several hours in advance. The method is based on the reconstruction of a chaotic strange attractor using four years of cloud absorption data obtained from half-hourly Meteosat infrared images from Northwestern Spain. An exhaustive nonlinear analysis of the time series was carried out to reconstruct the phase space of the underlying chaotic attractor. The forecast values are used by a non-hydrostatic meteorological model ARPS for daily weather prediction and their results compared with surface temperature measurements from a meteorological station and a vertical sounding. The effect of noise in the time series is analyzed in terms of the prediction results.Key words: Meterology and atmospheric dynamics (mesoscale meteorology; general – General (new fields

  11. Application of nonlinear forecasting techniques for meteorological modeling

    Directory of Open Access Journals (Sweden)

    V. Pérez-Muñuzuri

    Full Text Available A nonlinear forecasting method was used to predict the behavior of a cloud coverage time series several hours in advance. The method is based on the reconstruction of a chaotic strange attractor using four years of cloud absorption data obtained from half-hourly Meteosat infrared images from Northwestern Spain. An exhaustive nonlinear analysis of the time series was carried out to reconstruct the phase space of the underlying chaotic attractor. The forecast values are used by a non-hydrostatic meteorological model ARPS for daily weather prediction and their results compared with surface temperature measurements from a meteorological station and a vertical sounding. The effect of noise in the time series is analyzed in terms of the prediction results.

    Key words: Meterology and atmospheric dynamics (mesoscale meteorology; general – General (new fields

  12. Forecasting corn production in Serbia using ARIMA model

    Directory of Open Access Journals (Sweden)

    Ilić Ivana

    2016-01-01

    Full Text Available Agricultural crop production is closely related to climate, as a decisive success factor. Temperature fluctuations and changes in the volume of precipitation are the main factors affecting the growth and development of crops, and, ultimately, the quantity produced. Corn is the most common crop necessary to provide for domestic needs, and a strategic product for export. Production of corn in the period from 1947 to 2014 in Serbia had an oscillatory trend, with significant jumps and falls in production. The subject of this paper is the forecasting of future trends in corn production in Serbia. Building on the subject, the purpose of this paper is to create the model for forecasting future corn production and establishing its trends.

  13. Forecasting Macedonian Business Cycle Turning Points Using Qual Var Model

    Directory of Open Access Journals (Sweden)

    Petrovska Magdalena

    2016-09-01

    Full Text Available This paper aims at assessing the usefulness of leading indicators in business cycle research and forecast. Initially we test the predictive power of the economic sentiment indicator (ESI within a static probit model as a leading indicator, commonly perceived to be able to provide a reliable summary of the current economic conditions. We further proceed analyzing how well an extended set of indicators performs in forecasting turning points of the Macedonian business cycle by employing the Qual VAR approach of Dueker (2005. In continuation, we evaluate the quality of the selected indicators in pseudo-out-of-sample context. The results show that the use of survey-based indicators as a complement to macroeconomic data work satisfactory well in capturing the business cycle developments in Macedonia.

  14. HOKF: High Order Kalman Filter for Epilepsy Forecasting Modeling.

    Science.gov (United States)

    Nguyen, Ngoc Anh Thi; Yang, Hyung-Jeong; Kim, Sunhee

    2017-08-01

    Epilepsy forecasting has been extensively studied using high-order time series obtained from scalp-recorded electroencephalography (EEG). An accurate seizure prediction system would not only help significantly improve patients' quality of life, but would also facilitate new therapeutic strategies to manage epilepsy. This paper thus proposes an improved Kalman Filter (KF) algorithm to mine seizure forecasts from neural activity by modeling three properties in the high-order EEG time series: noise, temporal smoothness, and tensor structure. The proposed High-Order Kalman Filter (HOKF) is an extension of the standard Kalman filter, for which higher-order modeling is limited. The efficient dynamic of HOKF system preserves the tensor structure of the observations and latent states. As such, the proposed method offers two main advantages: (i) effectiveness with HOKF results in hidden variables that capture major evolving trends suitable to predict neural activity, even in the presence of missing values; and (ii) scalability in that the wall clock time of the HOKF is linear with respect to the number of time-slices of the sequence. The HOKF algorithm is examined in terms of its effectiveness and scalability by conducting forecasting and scalability experiments with a real epilepsy EEG dataset. The results of the simulation demonstrate the superiority of the proposed method over the original Kalman Filter and other existing methods. Copyright © 2017 Elsevier B.V. All rights reserved.

  15. Ten-year operational dust forecasting - Recent model development and future plans

    International Nuclear Information System (INIS)

    Kallos, G; Spyrou, C; Astitha, M; Mitsakou, C; Solomos, S; Kushta, J; Pytharoulis, I; Katsafados, P; Mavromatidis, E; Papantoniou, N; Vlastou, G

    2009-01-01

    The Sahara desert is one of the major sources of mineral dust on Earth, producing up to 2x10 8 t yr- 1 . A combined effort has been devoted during the last ten years at the University of Athens (UOA) from the Atmospheric Modeling and Weather Forecasting Group (AM and WFG) to the development of an analysis and forecasting tool that will provide early warning of Saharan dust outbreaks. The developed tool is the SKIRON limited-area forecasting system, based on the Eta limited area modeling system with embedded algorithms describing the dust cycle. A new version of the model is currently available, with extra features like eight-size particle bins, radiative transfer corrections, new dust source identification and utilization of rocky soil characterization and incorporation of more accurate deposition schemes. The new version of SKIRON modeling system is coupled with the photochemical model CAMx in order to study processes like the shading effect of dust particles on photochemical processes and the production of second and third generation of aerosols. Moreover, another new development in the AM and WFG is based on the RAMS model, with the incorporation of processes like dust and sea-salt production, gas and aqueous phase chemistry and particle formation. In this study, the major characteristics of the developed (and under development) modeling systems are presented, as well as the spatiotemporal distribution of the transported dust amounts, the interaction with anthropogenically-produced particles and the potential implications on radiative transfer.

  16. Ten-year operational dust forecasting - Recent model development and future plans

    Energy Technology Data Exchange (ETDEWEB)

    Kallos, G; Spyrou, C; Astitha, M; Mitsakou, C; Solomos, S; Kushta, J; Pytharoulis, I; Katsafados, P; Mavromatidis, E; Papantoniou, N; Vlastou, G [University of Athens, School of Physics, Atmospheric Modeling and Weather Forecasting Group - UOA/AM and WFG, University Campus, Bldg. PHYS-V, Athens 15784 (Greece)], E-mail: kallos@mg.uoa.gr

    2009-03-01

    The Sahara desert is one of the major sources of mineral dust on Earth, producing up to 2x10{sup 8} t yr-{sup 1}. A combined effort has been devoted during the last ten years at the University of Athens (UOA) from the Atmospheric Modeling and Weather Forecasting Group (AM and WFG) to the development of an analysis and forecasting tool that will provide early warning of Saharan dust outbreaks. The developed tool is the SKIRON limited-area forecasting system, based on the Eta limited area modeling system with embedded algorithms describing the dust cycle. A new version of the model is currently available, with extra features like eight-size particle bins, radiative transfer corrections, new dust source identification and utilization of rocky soil characterization and incorporation of more accurate deposition schemes. The new version of SKIRON modeling system is coupled with the photochemical model CAMx in order to study processes like the shading effect of dust particles on photochemical processes and the production of second and third generation of aerosols. Moreover, another new development in the AM and WFG is based on the RAMS model, with the incorporation of processes like dust and sea-salt production, gas and aqueous phase chemistry and particle formation. In this study, the major characteristics of the developed (and under development) modeling systems are presented, as well as the spatiotemporal distribution of the transported dust amounts, the interaction with anthropogenically-produced particles and the potential implications on radiative transfer.

  17. Frost Monitoring and Forecasting Using MODIS Land Surface Temperature Data and a Numerical Weather Prediction Model Forecasts for Eastern Africa

    Science.gov (United States)

    Kabuchanga, Eric; Flores, Africa; Malaso, Susan; Mungai, John; Sakwa, Vincent; Shaka, Ayub; Limaye, Ashutosh

    2014-01-01

    Frost is a major challenge across Eastern Africa, severely impacting agricultural farms. Frost damages have wide ranging economic implications on tea and coffee farms, which represent a major economic sector. Early monitoring and forecasting will enable farmers to take preventive actions to minimize the losses. Although clearly important, timely information on when to protect crops from freezing is relatively limited. MODIS Land Surface Temperature (LST) data, derived from NASA's Terra and Aqua satellites, and 72-hr weather forecasts from the Kenya Meteorological Service's operational Weather Research Forecast model are enabling the Regional Center for Mapping of Resources for Development (RCMRD) and the Tea Research Foundation of Kenya to provide timely information to farmers in the region. This presentation will highlight an ongoing collaboration among the Kenya Meteorological Service, RCMRD, and the Tea Research Foundation of Kenya to identify frost events and provide farmers with potential frost forecasts in Eastern Africa.

  18. Recursive wind speed forecasting based on Hammerstein Auto-Regressive model

    International Nuclear Information System (INIS)

    Ait Maatallah, Othman; Achuthan, Ajit; Janoyan, Kerop; Marzocca, Pier

    2015-01-01

    Highlights: • Developed a new recursive WSF model for 1–24 h horizon based on Hammerstein model. • Nonlinear HAR model successfully captured chaotic dynamics of wind speed time series. • Recursive WSF intrinsic error accumulation corrected by applying rotation. • Model verified for real wind speed data from two sites with different characteristics. • HAR model outperformed both ARIMA and ANN models in terms of accuracy of prediction. - Abstract: A new Wind Speed Forecasting (WSF) model, suitable for a short term 1–24 h forecast horizon, is developed by adapting Hammerstein model to an Autoregressive approach. The model is applied to real data collected for a period of three years (2004–2006) from two different sites. The performance of HAR model is evaluated by comparing its prediction with the classical Autoregressive Integrated Moving Average (ARIMA) model and a multi-layer perceptron Artificial Neural Network (ANN). Results show that the HAR model outperforms both the ARIMA model and ANN model in terms of root mean square error (RMSE), mean absolute error (MAE), and Mean Absolute Percentage Error (MAPE). When compared to the conventional models, the new HAR model can better capture various wind speed characteristics, including asymmetric (non-gaussian) wind speed distribution, non-stationary time series profile, and the chaotic dynamics. The new model is beneficial for various applications in the renewable energy area, particularly for power scheduling

  19. Models for Train Passenger Forecasting of Java and Sumatra

    Science.gov (United States)

    Sartono

    2017-04-01

    People tend to take public transportation to avoid high traffic, especially in Java. In Jakarta, the number of railway passengers is over than the capacity of the train at peak time. This is an opportunity as well as a challenge. If it is managed well then the company can get high profit. Otherwise, it may lead to disaster. This article discusses models for the train passengers, hence, finding the reasonable models to make a prediction overtimes. The Box-Jenkins method is occupied to develop a basic model. Then, this model is compared to models obtained using exponential smoothing method and regression method. The result shows that Holt-Winters model is better to predict for one-month, three-month, and six-month ahead for the passenger in Java. In addition, SARIMA(1,1,0)(2,0,0) is more accurate for nine-month and twelve-month oversee. On the other hand, for Sumatra passenger forecasting, SARIMA(1,1,1)(0,0,2) gives a better approximation for one-month ahead, and ARIMA model is best for three-month ahead prediction. The rest, Trend Seasonal and Liner Model has the least of RMSE to forecast for six-month, nine-month, and twelve-month ahead.

  20. Forecast model of landslides in a short time

    International Nuclear Information System (INIS)

    Sanchez Lopez, Reinaldo

    2006-01-01

    The IDEAM in development of their functions as member of the national technical committee for the prevention and disasters attention (SNPAD) accomplishes the follow-up, monitoring and forecast in real time of the environmental dynamics that in extreme situations constitute threats and natural risks. One of the frequent dynamics and of greater impact is related to landslides, those that affect persistently the life of the persons, the infrastructure, the socioeconomic activities and the balance of the environment. The landslide in Colombia and in the world are caused mainly by effects of the rain, due to that, IDEAM has come developing forecast model, as an instrument for risk management in a short time. This article presents aspects related to their structure, operation, temporary space resolution, products, results, achievements and projections of the model. Conceptually, the model is support by the principle of the dynamic temporary - space, of the processes that consolidate natural hazards, particularly in areas where the man has come building the risk. Structurally, the model is composed by two sub-models; the general susceptibility of the earthly model and the critical rain model as a denotative factor, that consolidate the hazard process. In real time, the model, works as a GIS, permitting the automatic zoning of the landslides hazard for issue public advisory warming to help makers decisions on the risk that cause frequently these events, in the country

  1. 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...

  2. Forecasting the mortality rates using Lee-Carter model and Heligman-Pollard model

    Science.gov (United States)

    Ibrahim, R. I.; Ngataman, N.; Abrisam, W. N. A. Wan Mohd

    2017-09-01

    Improvement in life expectancies has driven further declines in mortality. The sustained reduction in mortality rates and its systematic underestimation has been attracting the significant interest of researchers in recent years because of its potential impact on population size and structure, social security systems, and (from an actuarial perspective) the life insurance and pensions industry worldwide. Among all forecasting methods, the Lee-Carter model has been widely accepted by the actuarial community and Heligman-Pollard model has been widely used by researchers in modelling and forecasting future mortality. Therefore, this paper only focuses on Lee-Carter model and Heligman-Pollard model. The main objective of this paper is to investigate how accurately these two models will perform using Malaysian data. Since these models involves nonlinear equations that are explicitly difficult to solve, the Matrix Laboratory Version 8.0 (MATLAB 8.0) software will be used to estimate the parameters of the models. Autoregressive Integrated Moving Average (ARIMA) procedure is applied to acquire the forecasted parameters for both models as the forecasted mortality rates are obtained by using all the values of forecasted parameters. To investigate the accuracy of the estimation, the forecasted results will be compared against actual data of mortality rates. The results indicate that both models provide better results for male population. However, for the elderly female population, Heligman-Pollard model seems to underestimate to the mortality rates while Lee-Carter model seems to overestimate to the mortality rates.

  3. Vaginal drug distribution modeling.

    Science.gov (United States)

    Katz, David F; Yuan, Andrew; Gao, Yajing

    2015-09-15

    This review presents and applies fundamental mass transport theory describing the diffusion and convection driven mass transport of drugs to the vaginal environment. It considers sources of variability in the predictions of the models. It illustrates use of model predictions of microbicide drug concentration distribution (pharmacokinetics) to gain insights about drug effectiveness in preventing HIV infection (pharmacodynamics). The modeling compares vaginal drug distributions after different gel dosage regimens, and it evaluates consequences of changes in gel viscosity due to aging. It compares vaginal mucosal concentration distributions of drugs delivered by gels vs. intravaginal rings. Finally, the modeling approach is used to compare vaginal drug distributions across species with differing vaginal dimensions. Deterministic models of drug mass transport into and throughout the vaginal environment can provide critical insights about the mechanisms and determinants of such transport. This knowledge, and the methodology that obtains it, can be applied and translated to multiple applications, involving the scientific underpinnings of vaginal drug distribution and the performance evaluation and design of products, and their dosage regimens, that achieve it. Copyright © 2015 Elsevier B.V. All rights reserved.

  4. Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting

    Directory of Open Access Journals (Sweden)

    Hong-Juan Li

    2013-04-01

    Full Text Available Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR, this paper presents a SVR model hybridized with the empirical mode decomposition (EMD method and auto regression (AR for electric load forecasting. The electric load data of the New South Wales (Australia market are employed for comparing the forecasting performances of different forecasting models. The results confirm the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.

  5. A Feature Fusion Based Forecasting Model for Financial Time Series

    Science.gov (United States)

    Guo, Zhiqiang; Wang, Huaiqing; Liu, Quan; Yang, Jie

    2014-01-01

    Predicting the stock market has become an increasingly interesting research area for both researchers and investors, and many prediction models have been proposed. In these models, feature selection techniques are used to pre-process the raw data and remove noise. In this paper, a prediction model is constructed to forecast stock market behavior with the aid of independent component analysis, canonical correlation analysis, and a support vector machine. First, two types of features are extracted from the historical closing prices and 39 technical variables obtained by independent component analysis. Second, a canonical correlation analysis method is utilized to combine the two types of features and extract intrinsic features to improve the performance of the prediction model. Finally, a support vector machine is applied to forecast the next day's closing price. The proposed model is applied to the Shanghai stock market index and the Dow Jones index, and experimental results show that the proposed model performs better in the area of prediction than other two similar models. PMID:24971455

  6. Short- and Long-Term Earthquake Forecasts Based on Statistical Models

    Science.gov (United States)

    Console, Rodolfo; Taroni, Matteo; Murru, Maura; Falcone, Giuseppe; Marzocchi, Warner

    2017-04-01

    The epidemic-type aftershock sequences (ETAS) models have been experimentally used to forecast the space-time earthquake occurrence rate during the sequence that followed the 2009 L'Aquila earthquake and for the 2012 Emilia earthquake sequence. These forecasts represented the two first pioneering attempts to check the feasibility of providing operational earthquake forecasting (OEF) in Italy. After the 2009 L'Aquila earthquake the Italian Department of Civil Protection nominated an International Commission on Earthquake Forecasting (ICEF) for the development of the first official OEF in Italy that was implemented for testing purposes by the newly established "Centro di Pericolosità Sismica" (CPS, the seismic Hazard Center) at the Istituto Nazionale di Geofisica e Vulcanologia (INGV). According to the ICEF guidelines, the system is open, transparent, reproducible and testable. The scientific information delivered by OEF-Italy is shaped in different formats according to the interested stakeholders, such as scientists, national and regional authorities, and the general public. The communication to people is certainly the most challenging issue, and careful pilot tests are necessary to check the effectiveness of the communication strategy, before opening the information to the public. With regard to long-term time-dependent earthquake forecast, the application of a newly developed simulation algorithm to Calabria region provided typical features in time, space and magnitude behaviour of the seismicity, which can be compared with those of the real observations. These features include long-term pseudo-periodicity and clustering of strong earthquakes, and a realistic earthquake magnitude distribution departing from the Gutenberg-Richter distribution in the moderate and higher magnitude range.

  7. An investigation of forecast horizon and observation fit’s influence on an econometric rate forecast model in the liner shipping industry

    DEFF Research Database (Denmark)

    Nielsen, Peter; Jiang, Liping; Rytter, Niels Gorm Malý

    2014-01-01

    This paper evaluates the influence of forecast horizon and observation fit on the robustness and performance of a specific freight rate forecast model used in the liner shipping industry. In the first stage of the research, a forecast model used to predict container freight rate development...... of the forecast horizon and observation fit and their interactions on the forecast model's performance. The results underline the complicated nature of creating a suitable forecast model by balancing business needs, a desire to fit a good model and achieve high accuracy. There is strong empirical evidence from...... this study; that a robust model is preferable, that overfitting is a true danger, and that a balance must be achieved between forecast horizon and the number of observations used to fit the model. In addition, methodological guidance has also been provided on how to test, design, and choose the superior...

  8. Day-ahead wind speed forecasting using f-ARIMA models

    International Nuclear Information System (INIS)

    Kavasseri, Rajesh G.; Seetharaman, Krithika

    2009-01-01

    With the integration of wind energy into electricity grids, it is becoming increasingly important to obtain accurate wind speed/power forecasts. Accurate wind speed forecasts are necessary to schedule dispatchable generation and tariffs in the day-ahead electricity market. This paper examines the use of fractional-ARIMA or f-ARIMA models to model, and forecast wind speeds on the day-ahead (24 h) and two-day-ahead (48 h) horizons. The models are applied to wind speed records obtained from four potential wind generation sites in North Dakota. The forecasted wind speeds are used in conjunction with the power curve of an operational (NEG MICON, 750 kW) turbine to obtain corresponding forecasts of wind power production. The forecast errors in wind speed/power are analyzed and compared with the persistence model. Results indicate that significant improvements in forecasting accuracy are obtained with the proposed models compared to the persistence method. (author)

  9. 2 Dimensional Hydrodynamic Flood Routing Analysis on Flood Forecasting Modelling for Kelantan River Basin

    Directory of Open Access Journals (Sweden)

    Azad Wan Hazdy

    2017-01-01

    Full Text Available Flood disaster occurs quite frequently in Malaysia and has been categorized as the most threatening natural disaster compared to landslides, hurricanes, tsunami, haze and others. A study by Department of Irrigation and Drainage (DID show that 9% of land areas in Malaysia are prone to flood which may affect approximately 4.9 million of the population. 2 Dimensional floods routing modelling demonstrate is turning out to be broadly utilized for flood plain display and is an extremely viable device for evaluating flood. Flood propagations can be better understood by simulating the flow and water level by using hydrodynamic modelling. The hydrodynamic flood routing can be recognized by the spatial complexity of the schematization such as 1D model and 2D model. It was found that most of available hydrological models for flood forecasting are more focus on short duration as compared to long duration hydrological model using the Probabilistic Distribution Moisture Model (PDM. The aim of this paper is to discuss preliminary findings on development of flood forecasting model using Probabilistic Distribution Moisture Model (PDM for Kelantan river basin. Among the findings discuss in this paper includes preliminary calibrated PDM model, which performed reasonably for the Dec 2014, but underestimated the peak flows. Apart from that, this paper also discusses findings on Soil Moisture Deficit (SMD and flood plain analysis. Flood forecasting is the complex process that begins with an understanding of the geographical makeup of the catchment and knowledge of the preferential regions of heavy rainfall and flood behaviour for the area of responsibility. Therefore, to decreases the uncertainty in the model output, so it is important to increase the complexity of the model.

  10. Overview, comparative assessment and recommendations of forecasting models for short-term water demand prediction

    CSIR Research Space (South Africa)

    Anele, AO

    2017-11-01

    Full Text Available -term water demand (STWD) forecasts. In view of this, an overview of forecasting methods for STWD prediction is presented. Based on that, a comparative assessment of the performance of alternative forecasting models from the different methods is studied. Times...

  11. 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)

  12. Mapping Distribution and Forecasting Invasion of Prosopis juliflora in Ethiopia's Afar Region

    Science.gov (United States)

    West, A. M.; Wakie, T.; Luizza, M.; Evangelista, P.

    2014-12-01

    Invasion of non-native species is among the most critical threats to natural ecosystems and economies world-wide. Mesquite (which includes some 45 species) is an invasive deciduous tree which is known to have an array of negative impacts on ecosystems and rural livelihoods in arid and semi-arid regions around the world, dominating millions of hectares of land in Asia, Africa, Australia and the Americas. In Ethiopia, Prosopis juliflora (the only reported mesquite) is the most pervasive plant invader, threatening local livelihoods and the country's unique biodiversity. Due to its rapid spread and persistence, P. juliflora has been ranked as one of the leading threats to traditional land use, exceeded only by drought and conflict. This project utilized NASA's Earth Observing System (EOS) data and species distribution modeling to map current infestations of P. juliflora in the Afar region of northeastern Ethiopia, and forecast its suitable habitat across the entire country. This project provided a time and cost-effective strategy for conducting risk assessments of invasive mesquite and subsequent monitoring and mitigation efforts by land managers and local communities.

  13. Perturbations of modeling and forecast of karachi coastal region seawater

    International Nuclear Information System (INIS)

    Hussain, M.A.; Abbas, S.; Ansari, M.R.K.; Zaffar, A.

    2013-01-01

    Global warming is now a stark reality affecting the humanity in many hazardous ways. Continuous floods in Pakistan in past two years are an eye opener in this regard. A great loss of property, agriculture and life as a result of these floods suggests for an intelligent monitoring of the future projections of climate change and global warming. This is necessary because the harmful impacts of natural hazards can be coped and alleviated with a good planning in advance. This monitoring demands for enhanced forecasting capabilities, use of better analytical techniques and a clear determination and study of the controlling factors. Karachi is a coastal city which is also the industrial hub of Pakistan. Moreover, it is among one of the largest metropolitans of the world. So expectedly is most suitable for the study of high level of complex natural and anthropogenic activities. It is peculiar in the sense that it has two summer seasons, a situation scarcely observable on the globe. Here, summer season seawater temperature fluctuations are studied with the help of Seasonal Autoregressive Integrated Moving Average (SARIMA) models and short- and long-term forecasts are made. Our short-term forecasts determine months for the summer wise temperature extremes. It appears that the months of May, June, July and August are the months of extreme temperature for the first summer and October is the month of extreme temperature for the second summer. The long-term forecasts predict that 2014, 2016, 2018, and 2019 will be the years of warm summers. The analysis appearing here would be useful for coastal-urban planners in emphasizing the impact of seawater extreme temperatures on urban industrial activities, etc. (author)

  14. Traffic congestion forecasting model for the INFORM System. Final report

    Energy Technology Data Exchange (ETDEWEB)

    Azarm, A.; Mughabghab, S.; Stock, D.

    1995-05-01

    This report describes a computerized traffic forecasting model, developed by Brookhaven National Laboratory (BNL) for a portion of the Long Island INFORM Traffic Corridor. The model has gone through a testing phase, and currently is able to make accurate traffic predictions up to one hour forward in time. The model will eventually take on-line traffic data from the INFORM system roadway sensors and make projections as to future traffic patterns, thus allowing operators at the New York State Department of Transportation (D.O.T.) INFORM Traffic Management Center to more optimally manage traffic. It can also form the basis of a travel information system. The BNL computer model developed for this project is called ATOP for Advanced Traffic Occupancy Prediction. The various modules of the ATOP computer code are currently written in Fortran and run on PC computers (pentium machine) faster than real time for the section of the INFORM corridor under study. The following summarizes the various routines currently contained in the ATOP code: Statistical forecasting of traffic flow and occupancy using historical data for similar days and time (long term knowledge), and the recent information from the past hour (short term knowledge). Estimation of the empirical relationships between traffic flow and occupancy using long and short term information. Mechanistic interpolation using macroscopic traffic models and based on the traffic flow and occupancy forecasted (item-1), and the empirical relationships (item-2) for the specific highway configuration at the time of simulation (construction, lane closure, etc.). Statistical routine for detection and classification of anomalies and their impact on the highway capacity which are fed back to previous items.

  15. Forecasting Lightning Threat using Cloud-Resolving Model Simulations

    Science.gov (United States)

    McCaul, Eugene W., Jr.; Goodman, Steven J.; LaCasse, Katherine M.; Cecil, Daniel J.

    2008-01-01

    Two new approaches are proposed and developed for making time and space dependent, quantitative short-term forecasts of lightning threat, and a blend of these approaches is devised that capitalizes on the strengths of each. The new methods are distinctive in that they are based entirely on the ice-phase hydrometeor fields generated by regional cloud-resolving numerical simulations, such as those produced by the WRF model. These methods are justified by established observational evidence linking aspects of the precipitating ice hydrometeor fields to total flash rates. The methods are straightforward and easy to implement, and offer an effective near-term alternative to the incorporation of complex and costly cloud electrification schemes into numerical models. One method is based on upward fluxes of precipitating ice hydrometeors in the mixed phase region at the-15 C level, while the second method is based on the vertically integrated amounts of ice hydrometeors in each model grid column. Each method can be calibrated by comparing domain-wide statistics of the peak values of simulated flash rate proxy fields against domain-wide peak total lightning flash rate density data from observations. Tests show that the first method is able to capture much of the temporal variability of the lightning threat, while the second method does a better job of depicting the areal coverage of the threat. Our blended solution is designed to retain most of the temporal sensitivity of the first method, while adding the improved spatial coverage of the second. Exploratory tests for selected North Alabama cases show that, because WRF can distinguish the general character of most convective events, our methods show promise as a means of generating quantitatively realistic fields of lightning threat. However, because the models tend to have more difficulty in predicting the instantaneous placement of storms, forecasts of the detailed location of the lightning threat based on single

  16. Short-term streamflow forecasting with global climate change implications A comparative study between genetic programming and neural network models

    Science.gov (United States)

    Makkeasorn, A.; Chang, N. B.; Zhou, X.

    2008-05-01

    SummarySustainable water resources management is a critically important priority across the globe. While water scarcity limits the uses of water in many ways, floods may also result in property damages and the loss of life. To more efficiently use the limited amount of water under the changing world or to resourcefully provide adequate time for flood warning, the issues have led us to seek advanced techniques for improving streamflow forecasting on a short-term basis. This study emphasizes the inclusion of sea surface temperature (SST) in addition to the spatio-temporal rainfall distribution via the Next Generation Radar (NEXRAD), meteorological data via local weather stations, and historical stream data via USGS gage stations to collectively forecast discharges in a semi-arid watershed in south Texas. Two types of artificial intelligence models, including genetic programming (GP) and neural network (NN) models, were employed comparatively. Four numerical evaluators were used to evaluate the validity of a suite of forecasting models. Research findings indicate that GP-derived streamflow forecasting models were generally favored in the assessment in which both SST and meteorological data significantly improve the accuracy of forecasting. Among several scenarios, NEXRAD rainfall data were proven its most effectiveness for a 3-day forecast, and SST Gulf-to-Atlantic index shows larger impacts than the SST Gulf-to-Pacific index on the streamflow forecasts. The most forward looking GP-derived models can even perform a 30-day streamflow forecast ahead of time with an r-square of 0.84 and RMS error 5.4 in our study.

  17. A COMPARATIVE STUDY OF FORECASTING MODELS FOR TREND AND SEASONAL TIME SERIES DOES COMPLEX MODEL ALWAYS YIELD BETTER FORECAST THAN SIMPLE MODELS

    Directory of Open Access Journals (Sweden)

    Suhartono Suhartono

    2005-01-01

    Full Text Available Many business and economic time series are non-stationary time series that contain trend and seasonal variations. Seasonality is a periodic and recurrent pattern caused by factors such as weather, holidays, or repeating promotions. A stochastic trend is often accompanied with the seasonal variations and can have a significant impact on various forecasting methods. In this paper, we will investigate and compare some forecasting methods for modeling time series with both trend and seasonal patterns. These methods are Winter's, Decomposition, Time Series Regression, ARIMA and Neural Networks models. In this empirical research, we study on the effectiveness of the forecasting performance, particularly to answer whether a complex method always give a better forecast than a simpler method. We use a real data, that is airline passenger data. The result shows that the more complex model does not always yield a better result than a simpler one. Additionally, we also find the possibility to do further research especially the use of hybrid model by combining some forecasting method to get better forecast, for example combination between decomposition (as data preprocessing and neural network model.

  18. Implementation of an atmospheric sulfur scheme in the HIRLAM regional weather forecast model

    International Nuclear Information System (INIS)

    Ekman, Annica

    2000-02-01

    Sulfur chemistry has been implemented into the regional weather forecast model HIRLAM in order to simulate sulfur fields during specific weather situations. The model calculates concentrations of sulfur dioxide in air (SO 2 (a)), sulfate in air (SO 4 (a)), sulfate in cloud water (SO 4 (aq)) and hydrogen peroxide (H 2 O 2 ). Modeled concentrations of SO 2 (a), SO 4 (a) and SO 4 (aq) in rain water are compared with observations for two weather situations, one winter case with an extensive stratiform cloud cover and one summer case with mostly convective clouds. A comparison of the weather forecast parameters precipitation, relative humidity, geopotential and temperature with observations is also performed. The results show that the model generally overpredicts the SO 2 (a) concentration and underpredicts the SO 4 (a) concentration. The agreement between modeled and observed SO 4 (aq) in rain water is poor. Calculated turnover times are approximately 1 day for SO 2 (a) and 2-2.5 days for SO 4 (a). For SO 2 (a) this is in accordance with earlier simulated global turnover times, but for SO 4 (a) it is substantially lower. Several sensitivity simulations show that the fractional mean bias and root mean square error decreases, mainly for SO 4 (a) and SO 4 (aq), if an additional oxidant for converting SO 2 (a) to SO 4 (a) is included in the model. All weather forecast parameters, except precipitation, agree better with observations than the sulfur variables do. Wet scavenging is responsible for about half of the deposited sulfur and in addition, a major part of the sulfate production occurs through in-cloud oxidation. Hence, the distribution of clouds and precipitation must be better simulated by the weather forecast model in order to improve the agreement between observed and simulated sulfur concentrations

  19. Electricity consumption forecasting in Italy using linear regression models

    Energy Technology Data Exchange (ETDEWEB)

    Bianco, Vincenzo; Manca, Oronzio; Nardini, Sergio [DIAM, Seconda Universita degli Studi di Napoli, Via Roma 29, 81031 Aversa (CE) (Italy)

    2009-09-15

    The influence of economic and demographic variables on the annual electricity consumption in Italy has been investigated with the intention to develop a long-term consumption forecasting model. The time period considered for the historical data is from 1970 to 2007. Different regression models were developed, using historical electricity consumption, gross domestic product (GDP), gross domestic product per capita (GDP per capita) and population. A first part of the paper considers the estimation of GDP, price and GDP per capita elasticities of domestic and non-domestic electricity consumption. The domestic and non-domestic short run price elasticities are found to be both approximately equal to -0.06, while long run elasticities are equal to -0.24 and -0.09, respectively. On the contrary, the elasticities of GDP and GDP per capita present higher values. In the second part of the paper, different regression models, based on co-integrated or stationary data, are presented. Different statistical tests are employed to check the validity of the proposed models. A comparison with national forecasts, based on complex econometric models, such as Markal-Time, was performed, showing that the developed regressions are congruent with the official projections, with deviations of {+-}1% for the best case and {+-}11% for the worst. These deviations are to be considered acceptable in relation to the time span taken into account. (author)

  20. Electricity consumption forecasting in Italy using linear regression models

    International Nuclear Information System (INIS)

    Bianco, Vincenzo; Manca, Oronzio; Nardini, Sergio

    2009-01-01

    The influence of economic and demographic variables on the annual electricity consumption in Italy has been investigated with the intention to develop a long-term consumption forecasting model. The time period considered for the historical data is from 1970 to 2007. Different regression models were developed, using historical electricity consumption, gross domestic product (GDP), gross domestic product per capita (GDP per capita) and population. A first part of the paper considers the estimation of GDP, price and GDP per capita elasticities of domestic and non-domestic electricity consumption. The domestic and non-domestic short run price elasticities are found to be both approximately equal to -0.06, while long run elasticities are equal to -0.24 and -0.09, respectively. On the contrary, the elasticities of GDP and GDP per capita present higher values. In the second part of the paper, different regression models, based on co-integrated or stationary data, are presented. Different statistical tests are employed to check the validity of the proposed models. A comparison with national forecasts, based on complex econometric models, such as Markal-Time, was performed, showing that the developed regressions are congruent with the official projections, with deviations of ±1% for the best case and ±11% for the worst. These deviations are to be considered acceptable in relation to the time span taken into account. (author)

  1. A New Hybrid Model Based on Data Preprocessing and an Intelligent Optimization Algorithm for Electrical Power System Forecasting

    Directory of Open Access Journals (Sweden)

    Ping Jiang

    2015-01-01

    Full Text Available The establishment of electrical power system cannot only benefit the reasonable distribution and management in energy resources, but also satisfy the increasing demand for electricity. The electrical power system construction is often a pivotal part in the national and regional economic development plan. This paper constructs a hybrid model, known as the E-MFA-BP model, that can forecast indices in the electrical power system, including wind speed, electrical load, and electricity price. Firstly, the ensemble empirical mode decomposition can be applied to eliminate the noise of original time series data. After data preprocessing, the back propagation neural network model is applied to carry out the forecasting. Owing to the instability of its structure, the modified firefly algorithm is employed to optimize the weight and threshold values of back propagation to obtain a hybrid model with higher forecasting quality. Three experiments are carried out to verify the effectiveness of the model. Through comparison with other traditional well-known forecasting models, and models optimized by other optimization algorithms, the experimental results demonstrate that the hybrid model has the best forecasting performance.

  2. Forecasting daily political opinion polls using the fractionally cointegrated VAR model

    DEFF Research Database (Denmark)

    Nielsen, Morten Ørregaard; Shibaev, Sergei S.

    We examine forecasting performance of the recent fractionally cointegrated vector autoregressive (FCVAR) model. We use daily polling data of political support in the United Kingdom for 2010-2015 and compare with popular competing models at several forecast horizons. Our findings show that the four...... trend from the model follows the vote share of the UKIP very closely, and we thus interpret it as a measure of Euro-skepticism in public opinion rather than an indicator of the more traditional left-right political spectrum. In terms of prediction of vote shares in the election, forecasts generated...... variants of the FCVAR model considered are generally ranked as the top four models in terms of forecast accuracy, and the FCVAR model significantly outperforms both univariate fractional models and the standard cointegrated VAR (CVAR) model at all forecast horizons. The relative forecast improvement...

  3. Out-of-sample Forecasting Performance of Won/Dollar Exchange Rate Return Volatility Model

    Directory of Open Access Journals (Sweden)

    Hojin Lee

    2009-06-01

    Full Text Available We compare the out-of-sample forecasting performance of volatility models using daily exchange rate for the KRW/USD during the period from 1992 to 2008. For various forecasting horizons, historical volatility models with a long memory tend to make more accurate forecasts. Especially, we carefully observe the difference between the EWMA and the GARCH(1,1 model. Our empirical finding that the GARCH model puts too much weight on recent observations relative to those in the past is consistent with prior evidence showing that asset market volatility has a long memory, such as Ding and Granger (1996. The forecasting model with the lowest MSFE and VaR forecast error among the models we consider is the EWMA model in which the forecast volatility for the coming period is a weighted average of recent squared return with exponentially declining weights. In terms of forecast accuracy, it clearly dominates the widely accepted GARCH and rolling window GARCH models. We also present a multiple comparison of the out-of-sample forecasting performance of volatility using the stationary bootstrap of Politis and Romano (1994. We find that the White's reality check for the GARCH(1,1 expanding window model and the FIGARCH(1,1 expanding window model clearly reject the null hypothesis and there exists a better model than the two benchmark models. On the other hand, when the EWMA model is the benchmark, the White's for all forecasting horizons are very high, which indicates the null hypothesis may not be rejected. The Hansen's report the same results. The GARCH(1,1 expanding window model and the FIGARCH(1,1 expanding window model are dominated by the best competing model in most of the forecasting horizons. In contrast, the RiskMetrics model seems to be the most preferred. We also consider combining the forecasts generated by averaging the six raw forecasts and a trimmed set of forecasts which calculate the mean of the four forecasts after disregarding the highest and

  4. Short-Term Wind Power Interval Forecasting Based on an EEMD-RT-RVM Model

    Directory of Open Access Journals (Sweden)

    Haixiang Zang

    2016-01-01

    Full Text Available Accurate short-term wind power forecasting is important for improving the security and economic success of power grids. Existing wind power forecasting methods are mostly types of deterministic point forecasting. Deterministic point forecasting is vulnerable to forecasting errors and cannot effectively deal with the random nature of wind power. In order to solve the above problems, we propose a short-term wind power interval forecasting model based on ensemble empirical mode decomposition (EEMD, runs test (RT, and relevance vector machine (RVM. First, in order to reduce the complexity of data, the original wind power sequence is decomposed into a plurality of intrinsic mode function (IMF components and residual (RES component by using EEMD. Next, we use the RT method to reconstruct the components and obtain three new components characterized by the fine-to-coarse order. Finally, we obtain the overall forecasting results (with preestablished confidence levels by superimposing the forecasting results of each new component. Our results show that, compared with existing methods, our proposed short-term interval forecasting method has less forecasting errors, narrower interval widths, and larger interval coverage percentages. Ultimately, our forecasting model is more suitable for engineering applications and other forecasting methods for new energy.

  5. A note on the multi model super ensemble technique for reducing forecast errors

    International Nuclear Information System (INIS)

    Kantha, L.; Carniel, S.; Sclavo, M.

    2008-01-01

    The multi model super ensemble (S E) technique has been used with considerable success to improve meteorological forecasts and is now being applied to ocean models. Although the technique has been shown to produce deterministic forecasts that can be superior to the individual models in the ensemble or a simple multi model ensemble forecast, there is a clear need to understand its strengths and limitations. This paper is an attempt to do so in simple, easily understood contexts. The results demonstrate that the S E forecast is almost always better than the simple ensemble forecast, the degree of improvement depending on the properties of the models in the ensemble. However, the skill of the S E forecast with respect to the true forecast depends on a number of factors, principal among which is the skill of the models in the ensemble. As can be expected, if the ensemble consists of models with poor skill, the S E forecast will also be poor, although better than the ensemble forecast. On the other hand, the inclusion of even a single skillful model in the ensemble increases the forecast skill significantly.

  6. Synergizing two NWP models to improve hub-height wind speed forecasts

    Energy Technology Data Exchange (ETDEWEB)

    Liu, H. [Ortech International, Mississauga, ON (Canada); Taylor, P. [York Univ., Toronto, ON (Canada)

    2010-07-01

    This PowerPoint presentation discussed some of the methods used to optimize hub-height wind speed forecasts. Statistical and physical forecast paradigms were considered. Forecast errors are often dictated by phase error, while refined NWP modelling is limited by data availability. A nested meso-scale NWP model was combined with a physical model to predict wind and power forecasts. Maps of data sources were included as well as equations used to derive predictions. Data from meteorological masts located near the Great Lakes were used to demonstrate the model. The results were compared with other modelling prediction methods. Forecasts obtained using the modelling approach can help operators in scheduling and trading procedures. Further research is being conducted to determine if the model can be used to improve ramp forecasts. tabs., figs.

  7. Extended-range forecast for the temporal distribution of clustering tropical cyclogenesis over the western North Pacific

    Science.gov (United States)

    Zhu, Zhiwei; Li, Tim; Bai, Long; Gao, Jianyun

    2017-11-01

    Based on outgoing longwave radiation (OLR), an index for clustering tropical cyclogenesis (CTC) over the western North Pacific (WNP) was defined. Around 76 % of total CTC events were generated during the active phase of the CTC index, and 38 % of the total active phase was concurrent with CTC events. For its continuous property, the CTC index was used as the representative predictand for extended-range forecasting the temporal distribution of CTC events. The predictability sources for CTC events were detected via correlation analyses of the previous 35-5-day lead atmospheric fields against the CTC index. The results showed that the geopotential height at different levels and the 200 hPa zonal wind over the global tropics possessed large predictability sources, whereas the predictability sources of other variables, e.g., OLR, zonal wind, and relatively vorticity at 850 hPa and relatively humility at 700 hPa, were mainly confined to the tropical Indian Ocean and western Pacific Ocean. Several spatial-temporal projection model (STPM) sets were constructed to carry out the extended-range forecast for the CTC index. By combining the output of STPMs separately conducted for the two dominant modes of intraseasonal variability, e.g., the 10-30 and the 30-80 day mode, useful forecast skill could be achieved for a 30-day lead time. The combined output successfully captured both the 10-30 and 30-80 day mode at least 10 days in advance. With a relatively low rate of false alarm, the STPM achieved hits for 80 % (69 %) of 54 CTC events during 2003-2014 at the 10-day (20-day) lead time, suggesting a practical value of the STPM for real-time forecasting WNP CTC events at an extended range.

  8. Evaluating winds and vertical wind shear from Weather Research and Forecasting model forecasts using seven planetary boundary layer schemes

    DEFF Research Database (Denmark)

    Draxl, Caroline; Hahmann, Andrea N.; Pena Diaz, Alfredo

    2014-01-01

    with different PBL parameterizations at one coastal site over western Denmark. The evaluation focuses on determining which PBL parameterization performs best for wind energy forecasting, and presenting a validation methodology that takes into account wind speed at different heights. Winds speeds at heights...... regarding wind energy at these levels partly depends on the formulation and implementation of planetary boundary layer (PBL) parameterizations in these models. This study evaluates wind speeds and vertical wind shears simulated by theWeather Research and Forecasting model using seven sets of simulations...

  9. Fuzzy logic-based analogue forecasting and hybrid modelling of horizontal visibility

    Science.gov (United States)

    Tuba, Zoltán; Bottyán, Zsolt

    2018-04-01

    Forecasting visibility is one of the greatest challenges in aviation meteorology. At the same time, high accuracy visibility forecasts can significantly reduce or make avoidable weather-related risk in aviation as well. To improve forecasting visibility, this research links fuzzy logic-based analogue forecasting and post-processed numerical weather prediction model outputs in hybrid forecast. Performance of analogue forecasting model was improved by the application of Analytic Hierarchy Process. Then, linear combination of the mentioned outputs was applied to create ultra-short term hybrid visibility prediction which gradually shifts the focus from statistical to numerical products taking their advantages during the forecast period. It gives the opportunity to bring closer the numerical visibility forecast to the observations even it is wrong initially. Complete verification of categorical forecasts was carried out; results are available for persistence and terminal aerodrome forecasts (TAF) as well in order to compare. The average value of Heidke Skill Score (HSS) of examined airports of analogue and hybrid forecasts shows very similar results even at the end of forecast period where the rate of analogue prediction in the final hybrid output is 0.1-0.2 only. However, in case of poor visibility (1000-2500 m), hybrid (0.65) and analogue forecasts (0.64) have similar average of HSS in the first 6 h of forecast period, and have better performance than persistence (0.60) or TAF (0.56). Important achievement that hybrid model takes into consideration physics and dynamics of the atmosphere due to the increasing part of the numerical weather prediction. In spite of this, its performance is similar to the most effective visibility forecasting methods and does not follow the poor verification results of clearly numerical outputs.

  10. Electricity generation modeling and photovoltaic forecasts in China

    Science.gov (United States)

    Li, Shengnan

    With the economic development of China, the demand for electricity generation is rapidly increasing. To explain electricity generation, we use gross GDP, the ratio of urban population to rural population, the average per capita income of urban residents, the electricity price for industry in Beijing, and the policy shift that took place in China. Ordinary least squares (OLS) is used to develop a model for the 1979--2009 period. During the process of designing the model, econometric methods are used to test and develop the model. The final model is used to forecast total electricity generation and assess the possible role of photovoltaic generation. Due to the high demand for resources and serious environmental problems, China is pushing to develop the photovoltaic industry. The system price of PV is falling; therefore, photovoltaics may be competitive in the future.

  11. A numerical storm surge forecast model with Kalman filter

    Institute of Scientific and Technical Information of China (English)

    Yu Fujiang; Zhang Zhanhai; Lin Yihua

    2001-01-01

    Kalman filter data assimilation technique is incorporated into a standard two-dimensional linear storm surge model. Imperfect model equation and imperfect meteorological forcimg are accounted for by adding noise terms to the momentum equations. The deterministic model output is corrected by using the available tidal gauge station data. The stationary Kalman filter algorithm for the model domain is calculated by an iterative procedure using specified information on the inaccuracies in the momentum equations and specified error information for the observations. An application to a real storm surge that occurred in the summer of 1956 in the East China Sea is performed by means of this data assimilation technique. The result shows that Kalman filter is useful for storm surge forecast and hindcast.

  12. Forecasting the Reference Evapotranspiration Using Time Series Model

    Directory of Open Access Journals (Sweden)

    H. Zare Abyaneh

    2016-10-01

    Full Text Available Introduction: Reference evapotranspiration is one of the most important factors in irrigation timing and field management. Moreover, reference evapotranspiration forecasting can play a vital role in future developments. Therefore in this study, the seasonal autoregressive integrated moving average (ARIMA model was used to forecast the reference evapotranspiration time series in the Esfahan, Semnan, Shiraz, Kerman, and Yazd synoptic stations. Materials and Methods: In the present study in all stations (characteristics of the synoptic stations are given in Table 1, the meteorological data, including mean, maximum and minimum air temperature, relative humidity, dry-and wet-bulb temperature, dew-point temperature, wind speed, precipitation, air vapor pressure and sunshine hours were collected from the Islamic Republic of Iran Meteorological Organization (IRIMO for the 41 years from 1965 to 2005. The FAO Penman-Monteith equation was used to calculate the monthly reference evapotranspiration in the five synoptic stations and the evapotranspiration time series were formed. The unit root test was used to identify whether the time series was stationary, then using the Box-Jenkins method, seasonal ARIMA models were applied to the sample data. Table 1. The geographical location and climate conditions of the synoptic stations Station\tGeographical location\tAltitude (m\tMean air temperature (°C\tMean precipitation (mm\tClimate, according to the De Martonne index classification Longitude (E\tLatitude (N Annual\tMin. and Max. Esfahan\t51° 40'\t32° 37'\t1550.4\t16.36\t9.4-23.3\t122\tArid Semnan\t53° 33'\t35° 35'\t1130.8\t18.0\t12.4-23.8\t140\tArid Shiraz\t52° 36'\t29° 32'\t1484\t18.0\t10.2-25.9\t324\tSemi-arid Kerman\t56° 58'\t30° 15'\t1753.8\t15.6\t6.7-24.6\t142\tArid Yazd\t54° 17'\t31° 54'\t1237.2\t19.2\t11.8-26.0\t61\tArid Results and Discussion: The monthly meteorological data were used as input for the Ref-ET software and monthly reference

  13. Case studies of extended model-based flood forecasting: prediction of dike strength and flood impacts

    Science.gov (United States)

    Stuparu, Dana; Bachmann, Daniel; Bogaard, Tom; Twigt, Daniel; Verkade, Jan; de Bruijn, Karin; de Leeuw, Annemargreet

    2017-04-01

    Flood forecasts, warning and emergency response are important components in flood risk management. Most flood forecasting systems use models to translate weather predictions to forecasted discharges or water levels. However, this information is often not sufficient for real time decisions. A sound understanding of the reliability of embankments and flood dynamics is needed to react timely and reduce the negative effects of the flood. Where are the weak points in the dike system? When, how much and where the water will flow? When and where is the greatest impact expected? Model-based flood impact forecasting tries to answer these questions by adding new dimensions to the existing forecasting systems by providing forecasted information about: (a) the dike strength during the event (reliability), (b) the flood extent in case of an overflow or a dike failure (flood spread) and (c) the assets at risk (impacts). This work presents three study-cases in which such a set-up is applied. Special features are highlighted. Forecasting of dike strength. The first study-case focusses on the forecast of dike strength in the Netherlands for the river Rhine branches Waal, Nederrijn and IJssel. A so-called reliability transformation is used to translate the predicted water levels at selected dike sections into failure probabilities during a flood event. The reliability of a dike section is defined by fragility curves - a summary of the dike strength conditional to the water level. The reliability information enhances the emergency management and inspections of embankments. Ensemble forecasting. The second study-case shows the setup of a flood impact forecasting system in Dumfries, Scotland. The existing forecasting system is extended with a 2D flood spreading model in combination with the Delft-FIAT impact model. Ensemble forecasts are used to make use of the uncertainty in the precipitation forecasts, which is useful to quantify the certainty of a forecasted flood event. From global

  14. Validation Test Report for the BioCast Optical Forecast Model Version 1.0

    Science.gov (United States)

    2015-04-09

    hour forecast sequence have been established for ten months of continuous daily forecasting sequence trails in the northern Gulf of Mexico and the...beam-c field) is combined with today’s NCOM hourly forecast currents to predict the turbidity distribution (c)1 in 24 hours. The following day’s...proxy for turbidity ) coupled with the currents derived from the NCOM, BioCast enables the currents to advect the turbidity pixel information

  15. Modeling Philippine Stock Exchange Composite Index Using Weighted Geometric Brownian Motion Forecasts

    Directory of Open Access Journals (Sweden)

    Gayo Willy

    2016-01-01

    Full Text Available Philippine Stock Exchange Composite Index (PSEi is the main stock index of the Philippine Stock Exchange (PSE. PSEi is computed using a weighted mean of the top 30 publicly traded companies in the Philippines, called component stocks. It provides a single value by which the performance of the Philippine stock market is measured. Unfortunately, these weights, which may vary for every trading day, are not disclosed by the PSE. In this paper, we propose a model of forecasting the PSEi by estimating the weights based on historical data and forecasting each component stock using Monte Carlo simulation based on a Geometric Brownian Motion (GBM assumption. The model performance is evaluated and its forecast compared is with the results using a direct GBM forecast of PSEi over different forecast periods. Results showed that the forecasts using WGBM will yield smaller error compared to direct GBM forecast of PSEi.

  16. Nonlinear time series modeling and forecasting the seismic data of the Hindu Kush region

    Science.gov (United States)

    Khan, Muhammad Yousaf; Mittnik, Stefan

    2018-01-01

    In this study, we extended the application of linear and nonlinear time models in the field of earthquake seismology and examined the out-of-sample forecast accuracy of linear Autoregressive (AR), Autoregressive Conditional Duration (ACD), Self-Exciting Threshold Autoregressive (SETAR), Threshold Autoregressive (TAR), Logistic Smooth Transition Autoregressive (LSTAR), Additive Autoregressive (AAR), and Artificial Neural Network (ANN) models for seismic data of the Hindu Kush region. We also extended the previous studies by using Vector Autoregressive (VAR) and Threshold Vector Autoregressive (TVAR) models and compared their forecasting accuracy with linear AR model. Unlike previous studies that typically consider the threshold model specifications by using internal threshold variable, we specified these models with external transition variables and compared their out-of-sample forecasting performance with the linear benchmark AR model. The modeling results show that time series models used in the present study are capable of capturing the dynamic structure present in the seismic data. The point forecast results indicate that the AR model generally outperforms the nonlinear models. However, in some cases, threshold models with external threshold variables specification produce more accurate forecasts, indicating that specification of threshold time series models is of crucial importance. For raw seismic data, the ACD model does not show an improved out-of-sample forecasting performance over the linear AR model. The results indicate that the AR model is the best forecasting device to model and forecast the raw seismic data of the Hindu Kush region.

  17. A spatiotemporal clustering model for the Third Uniform California Earthquake Rupture Forecast (UCERF3‐ETAS): Toward an operational earthquake forecast

    Science.gov (United States)

    Field, Edward; Milner, Kevin R.; Hardebeck, Jeanne L.; Page, Morgan T.; van der Elst, Nicholas; Jordan, Thomas H.; Michael, Andrew J.; Shaw, Bruce E.; Werner, Maximillan J.

    2017-01-01

    We, the ongoing Working Group on California Earthquake Probabilities, present a spatiotemporal clustering model for the Third Uniform California Earthquake Rupture Forecast (UCERF3), with the goal being to represent aftershocks, induced seismicity, and otherwise triggered events as a potential basis for operational earthquake forecasting (OEF). Specifically, we add an epidemic‐type aftershock sequence (ETAS) component to the previously published time‐independent and long‐term time‐dependent forecasts. This combined model, referred to as UCERF3‐ETAS, collectively represents a relaxation of segmentation assumptions, the inclusion of multifault ruptures, an elastic‐rebound model for fault‐based ruptures, and a state‐of‐the‐art spatiotemporal clustering component. It also represents an attempt to merge fault‐based forecasts with statistical seismology models, such that information on fault proximity, activity rate, and time since last event are considered in OEF. We describe several unanticipated challenges that were encountered, including a need for elastic rebound and characteristic magnitude–frequency distributions (MFDs) on faults, both of which are required to get realistic triggering behavior. UCERF3‐ETAS produces synthetic catalogs of M≥2.5 events, conditioned on any prior M≥2.5 events that are input to the model. We evaluate results with respect to both long‐term (1000 year) simulations as well as for 10‐year time periods following a variety of hypothetical scenario mainshocks. Although the results are very plausible, they are not always consistent with the simple notion that triggering probabilities should be greater if a mainshock is located near a fault. Important factors include whether the MFD near faults includes a significant characteristic earthquake component, as well as whether large triggered events can nucleate from within the rupture zone of the mainshock. Because UCERF3‐ETAS has many sources of uncertainty, as

  18. The multi temporal/multi-model approach to predictive uncertainty assessment in real-time flood forecasting

    Science.gov (United States)

    Barbetta, Silvia; Coccia, Gabriele; Moramarco, Tommaso; Brocca, Luca; Todini, Ezio

    2017-08-01

    This work extends the multi-temporal approach of the Model Conditional Processor (MCP-MT) to the multi-model case and to the four Truncated Normal Distributions (TNDs) approach, demonstrating the improvement on the single-temporal one. The study is framed in the context of probabilistic Bayesian decision-making that is appropriate to take rational decisions on uncertain future outcomes. As opposed to the direct use of deterministic forecasts, the probabilistic forecast identifies a predictive probability density function that represents a fundamental knowledge on future occurrences. The added value of MCP-MT is the identification of the probability that a critical situation will happen within the forecast lead-time and when, more likely, it will occur. MCP-MT is thoroughly tested for both single-model and multi-model configurations at a gauged site on the Tiber River, central Italy. The stages forecasted by two operative deterministic models, STAFOM-RCM and MISDc, are considered for the study. The dataset used for the analysis consists of hourly data from 34 flood events selected on a time series of six years. MCP-MT improves over the original models' forecasts: the peak overestimation and the rising limb delayed forecast, characterizing MISDc and STAFOM-RCM respectively, are significantly mitigated, with a reduced mean error on peak stage from 45 to 5 cm and an increased coefficient of persistence from 0.53 up to 0.75. The results show that MCP-MT outperforms the single-temporal approach and is potentially useful for supporting decision-making because the exceedance probability of hydrometric thresholds within a forecast horizon and the most probable flooding time can be estimated.

  19. Improved stratospheric atmosphere forecasts in the general circulation model through a methane oxidation parametrization

    Science.gov (United States)

    Wang, S.; Jun, Z.

    2017-12-01

    Climatic characteristics of tropical stratospheric methane have been well researched using various satellite data, and numerical simulations have furtherly conducted using chemical climatic models, while the impact of stratospheric methane oxidation on distribution of water vapor is not paid enough attention in general circulation models. Simulated values of water vapour in the tropical upper stratosphere, and throughout much of the extratropical stratosphere, were too low. Something must be done to remedy this deficiency in order to producing realistic stratospheric water vapor using a general circulation model including the whole stratosphere. Introduction of a simple parametrization of the upper-stratospheric moisture source due to methane oxidation and a sink due to photolysis in the mesosphere was conducted. Numerical simulations and analysis of the influence of stratospheric methane on the prediction of tropical stratospheric moisture and temperature fields were carried out. This study presents the advantages of methane oxidation parametrization in producing a realistic distribution of water vapour in the tropical stratosphere and analyzes the impact of methane chemical process on the general circulation model using two storm cases including a heavy rain in South China and a typhoon caused tropical storm.It is obvious that general circulation model with methane oxidation parametrization succeeds in simulating the water vapor and temperature in stratosphere. The simulating rain center value of contrast experiment is increased up to 10% than that of the control experiment. Introduction of methane oxidation parametrization has modified the distribution of water vapour and then producing a broadly realistic distribution of temperature. Objective weather forecast verifications have been performed using simulating results of one month, which demonstrate somewhat positive effects on the model skill. There is a certain extent impact of methane oxidation

  20. Application of SARIMA model to forecasting monthly flows in Waterval River, South Africa

    Directory of Open Access Journals (Sweden)

    Tadesse Kassahun Birhanu

    2017-12-01

    Full Text Available Knowledge of future river flow information is fundamental for development and management of a river system. In this study, Waterval River flow was forecasted by SARIMA model using GRETL statistical software. Mean monthly flows from 1960 to 2016 were used for modelling and forecasting. Different unit root and Mann–Kendall trend analysis proved the stationarity of the observed flow time series. Based on seasonally differenced correlogram characteristics, different SARIMA models were evaluated; their parameters were optimized, and diagnostic check up of forecasts was made using white noise and heteroscedasticity tests. Finally, based on minimum Akaike Information (AI and Hannan–Quinn (HQ criteria, SARIMA (3, 0, 2 x (3, 1, 312 model was selected for Waterval River flow forecasting. Comparison of forecast performance of SARIMA models with that of computational intelligent forecasting techniques was recommended for future study.

  1. A multi-tiered time-series modelling approach to forecasting respiratory syncytial virus incidence at the local level.

    Science.gov (United States)

    Spaeder, M C; Fackler, J C

    2012-04-01

    Respiratory syncytial virus (RSV) is the most common cause of documented viral respiratory infections, and the leading cause of hospitalization, in young children. We performed a retrospective time-series analysis of all patients aged Forecasting models of weekly RSV incidence for the local community, inpatient paediatric hospital and paediatric intensive-care unit (PICU) were created. Ninety-five percent confidence intervals calculated around our models' 2-week forecasts were accurate to ±9·3, ±7·5 and ±1·5 cases/week for the local community, inpatient hospital and PICU, respectively. Our results suggest that time-series models may be useful tools in forecasting the burden of RSV infection at the local and institutional levels, helping communities and institutions to optimize distribution of resources based on the changing burden and severity of illness in their respective communities.

  2. 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

  3. Daily air quality index forecasting with hybrid models: A case in China

    International Nuclear Information System (INIS)

    Zhu, Suling; Lian, Xiuyuan; Liu, Haixia; Hu, Jianming; Wang, Yuanyuan; Che, Jinxing

    2017-01-01

    Air quality is closely related to quality of life. Air pollution forecasting plays a vital role in air pollution warnings and controlling. However, it is difficult to attain accurate forecasts for air pollution indexes because the original data are non-stationary and chaotic. The existing forecasting methods, such as multiple linear models, autoregressive integrated moving average (ARIMA) and support vector regression (SVR), cannot fully capture the information from series of pollution indexes. Therefore, new effective techniques need to be proposed to forecast air pollution indexes. The main purpose of this research is to develop effective forecasting models for regional air quality indexes (AQI) to address the problems above and enhance forecasting accuracy. Therefore, two hybrid models (EMD-SVR-Hybrid and EMD-IMFs-Hybrid) are proposed to forecast AQI data. The main steps of the EMD-SVR-Hybrid model are as follows: the data preprocessing technique EMD (empirical mode decomposition) is utilized to sift the original AQI data to obtain one group of smoother IMFs (intrinsic mode functions) and a noise series, where the IMFs contain the important information (level, fluctuations and others) from the original AQI series. LS-SVR is applied to forecast the sum of the IMFs, and then, S-ARIMA (seasonal ARIMA) is employed to forecast the residual sequence of LS-SVR. In addition, EMD-IMFs-Hybrid first separately forecasts the IMFs via statistical models and sums the forecasting results of the IMFs as EMD-IMFs. Then, S-ARIMA is employed to forecast the residuals of EMD-IMFs. To certify the proposed hybrid model, AQI data from June 2014 to August 2015 collected from Xingtai in China are utilized as a test case to investigate the empirical research. In terms of some of the forecasting assessment measures, the AQI forecasting results of Xingtai show that the two proposed hybrid models are superior to ARIMA, SVR, GRNN, EMD-GRNN, Wavelet-GRNN and Wavelet-SVR. Therefore, the

  4. Initialization and Predictability of a Coupled ENSO Forecast Model

    Science.gov (United States)

    Chen, Dake; Zebiak, Stephen E.; Cane, Mark A.; Busalacchi, Antonio J.

    1997-01-01

    The skill of a coupled ocean-atmosphere model in predicting ENSO has recently been improved using a new initialization procedure in which initial conditions are obtained from the coupled model, nudged toward observations of wind stress. The previous procedure involved direct insertion of wind stress observations, ignoring model feedback from ocean to atmosphere. The success of the new scheme is attributed to its explicit consideration of ocean-atmosphere coupling and the associated reduction of "initialization shock" and random noise. The so-called spring predictability barrier is eliminated, suggesting that such a barrier is not intrinsic to the real climate system. Initial attempts to generalize the nudging procedure to include SST were not successful; possible explanations are offered. In all experiments forecast skill is found to be much higher for the 1980s than for the 1970s and 1990s, suggesting decadal variations in predictability.

  5. 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)

  6. 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

  7. Probabilistic Forecast of Wind Power Generation by Stochastic Differential Equation Models

    KAUST Repository

    Elkantassi, Soumaya

    2017-04-01

    Reliable forecasting of wind power generation is crucial to optimal control of costs in generation of electricity with respect to the electricity demand. Here, we propose and analyze stochastic wind power forecast models described by parametrized stochastic differential equations, which introduce appropriate fluctuations in numerical forecast outputs. We use an approximate maximum likelihood method to infer the model parameters taking into account the time correlated sets of data. Furthermore, we study the validity and sensitivity of the parameters for each model. We applied our models to Uruguayan wind power production as determined by historical data and corresponding numerical forecasts for the period of March 1 to May 31, 2016.

  8. Artificial Neural Network versus Linear Models Forecasting Doha Stock Market

    Science.gov (United States)

    Yousif, Adil; Elfaki, Faiz

    2017-12-01

    The purpose of this study is to determine the instability of Doha stock market and develop forecasting models. Linear time series models are used and compared with a nonlinear Artificial Neural Network (ANN) namely Multilayer Perceptron (MLP) Technique. It aims to establish the best useful model based on daily and monthly data which are collected from Qatar exchange for the period starting from January 2007 to January 2015. Proposed models are for the general index of Qatar stock exchange and also for the usages in other several sectors. With the help of these models, Doha stock market index and other various sectors were predicted. The study was conducted by using various time series techniques to study and analyze data trend in producing appropriate results. After applying several models, such as: Quadratic trend model, double exponential smoothing model, and ARIMA, it was concluded that ARIMA (2,2) was the most suitable linear model for the daily general index. However, ANN model was found to be more accurate than time series models.

  9. Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China.

    Directory of Open Access Journals (Sweden)

    Wei Wu

    Full Text Available Cases of hemorrhagic fever with renal syndrome (HFRS are widely distributed in eastern Asia, especially in China, Russia, and Korea. It is proved to be a difficult task to eliminate HFRS completely because of the diverse animal reservoirs and effects of global warming. Reliable forecasting is useful for the prevention and control of HFRS.Two hybrid models, one composed of nonlinear autoregressive neural network (NARNN and autoregressive integrated moving average (ARIMA the other composed of generalized regression neural network (GRNN and ARIMA were constructed to predict the incidence of HFRS in the future one year. Performances of the two hybrid models were compared with ARIMA model.The ARIMA, ARIMA-NARNN ARIMA-GRNN model fitted and predicted the seasonal fluctuation well. Among the three models, the mean square error (MSE, mean absolute error (MAE and mean absolute percentage error (MAPE of ARIMA-NARNN hybrid model was the lowest both in modeling stage and forecasting stage. As for the ARIMA-GRNN hybrid model, the MSE, MAE and MAPE of modeling performance and the MSE and MAE of forecasting performance were less than the ARIMA model, but the MAPE of forecasting performance did not improve.Developing and applying the ARIMA-NARNN hybrid model is an effective method to make us better understand the epidemic characteristics of HFRS and could be helpful to the prevention and control of HFRS.

  10. Day-Ahead Wind Power Forecasting Using a Two-Stage Hybrid Modeling Approach Based on SCADA and Meteorological Information, and Evaluating the Impact of Input-Data Dependency on Forecasting Accuracy

    Directory of Open Access Journals (Sweden)

    Dehua Zheng

    2017-12-01

    Full Text Available The power generated by wind generators is usually associated with uncertainties, due to the intermittency of wind speed and other weather variables. This creates a big challenge for transmission system operators (TSOs and distribution system operators (DSOs in terms of connecting, controlling and managing power networks with high-penetration wind energy. Hence, in these power networks, accurate wind power forecasts are essential for their reliable and efficient operation. They support TSOs and DSOs in enhancing the control and management of the power network. In this paper, a novel two-stage hybrid approach based on the combination of the Hilbert-Huang transform (HHT, genetic algorithm (GA and artificial neural network (ANN is proposed for day-ahead wind power forecasting. The approach is composed of two stages. The first stage utilizes numerical weather prediction (NWP meteorological information to predict wind speed at the exact site of the wind farm. The second stage maps actual wind speed vs. power characteristics recorded by SCADA. Then, the wind speed forecast in the first stage for the future day is fed to the second stage to predict the future day’s wind power. Comparative selection of input-data parameter sets for the forecasting model and impact analysis of input-data dependency on forecasting accuracy have also been studied. The proposed approach achieves significant forecasting accuracy improvement compared with three other artificial intelligence-based forecasting approaches and a benchmark model using the smart persistence method.

  11. Monthly streamflow forecasting based on hidden Markov model and Gaussian Mixture Regression

    Science.gov (United States)

    Liu, Yongqi; Ye, Lei; Qin, Hui; Hong, Xiaofeng; Ye, Jiajun; Yin, Xingli

    2018-06-01

    Reliable streamflow forecasts can be highly valuable for water resources planning and management. In this study, we combined a hidden Markov model (HMM) and Gaussian Mixture Regression (GMR) for probabilistic monthly streamflow forecasting. The HMM is initialized using a kernelized K-medoids clustering method, and the Baum-Welch algorithm is then executed to learn the model parameters. GMR derives a conditional probability distribution for the predictand given covariate information, including the antecedent flow at a local station and two surrounding stations. The performance of HMM-GMR was verified based on the mean square error and continuous ranked probability score skill scores. The reliability of the forecasts was assessed by examining the uniformity of the probability integral transform values. The results show that HMM-GMR obtained reasonably high skill scores and the uncertainty spread was appropriate. Different HMM states were assumed to be different climate conditions, which would lead to different types of observed values. We demonstrated that the HMM-GMR approach can handle multimodal and heteroscedastic data.

  12. Assessment of summer rainfall forecast skill in the Intra-Americas in GFDL high and low-resolution models

    Science.gov (United States)

    Krishnamurthy, Lakshmi; Muñoz, Ángel G.; Vecchi, Gabriel A.; Msadek, Rym; Wittenberg, Andrew T.; Stern, Bill; Gudgel, Rich; Zeng, Fanrong

    2018-05-01

    The Caribbean low-level jet (CLLJ) is an important component of the atmospheric circulation over the Intra-Americas Sea (IAS) which impacts the weather and climate both locally and remotely. It influences the rainfall variability in the Caribbean, Central America, northern South America, the tropical Pacific and the continental Unites States through the transport of moisture. We make use of high-resolution coupled and uncoupled models from the Geophysical Fluid Dynamics Laboratory (GFDL) to investigate the simulation of the CLLJ and its teleconnections and further compare with low-resolution models. The high-resolution coupled model FLOR shows improvements in the simulation of the CLLJ and its teleconnections with rainfall and SST over the IAS compared to the low-resolution coupled model CM2.1. The CLLJ is better represented in uncoupled models (AM2.1 and AM2.5) forced with observed sea-surface temperatures (SSTs), emphasizing the role of SSTs in the simulation of the CLLJ. Further, we determine the forecast skill for observed rainfall using both high- and low-resolution predictions of rainfall and SSTs for the July-August-September season. We determine the role of statistical correction of model biases, coupling and horizontal resolution on the forecast skill. Statistical correction dramatically improves area-averaged forecast skill. But the analysis of spatial distribution in skill indicates that the improvement in skill after statistical correction is region dependent. Forecast skill is sensitive to coupling in parts of the Caribbean, Central and northern South America, and it is mostly insensitive over North America. Comparison of forecast skill between high and low-resolution coupled models does not show any dramatic difference. However, uncoupled models show improvement in the area-averaged skill in the high-resolution atmospheric model compared to lower resolution model. Understanding and improving the forecast skill over the IAS has important implications

  13. 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

  14. WRF-Fire: coupled weather-wildland fire modeling with the weather research and forecasting model

    Science.gov (United States)

    Janice L. Coen; Marques Cameron; John Michalakes; Edward G. Patton; Philip J. Riggan; Kara M. Yedinak

    2012-01-01

    A wildland fire behavior module (WRF-Fire) was integrated into the Weather Research and Forecasting (WRF) public domain numerical weather prediction model. The fire module is a surface fire behavior model that is two-way coupled with the atmospheric model. Near-surface winds from the atmospheric model are interpolated to a finer fire grid and used, with fuel properties...

  15. Probabilistic methods for seasonal forecasting in a changing climate: Cox-type regression models

    NARCIS (Netherlands)

    Maia, A.H.N.; Meinke, H.B.

    2010-01-01

    For climate risk management, cumulative distribution functions (CDFs) are an important source of information. They are ideally suited to compare probabilistic forecasts of primary (e.g. rainfall) or secondary data (e.g. crop yields). Summarised as CDFs, such forecasts allow an easy quantitative

  16. An analysis of seasonal predictability in coupled model forecasts

    Energy Technology Data Exchange (ETDEWEB)

    Peng, P.; Wang, W. [NOAA, Climate Prediction Center, Washington, DC (United States); Kumar, A. [NOAA, Climate Prediction Center, Washington, DC (United States); NCEP/NWS/NOAA, Climate Prediction Center, Camp Springs, MD (United States)

    2011-02-15

    In the recent decade, operational seasonal prediction systems based on initialized coupled models have been developed. An analysis of how the predictability of seasonal means in the initialized coupled predictions evolves with lead-time is presented. Because of the short lead-time, such an analysis for the temporal behavior of seasonal predictability involves a mix of both the predictability of the first and the second kind. The analysis focuses on the lead-time dependence of ensemble mean variance, and the forecast spread. Further, the analysis is for a fixed target season of December-January-February, and is for sea surface temperature, rainfall, and 200-mb height. The analysis is based on a large set of hindcasts from an initialized coupled seasonal prediction system. Various aspects of predictability of the first and the second kind are highlighted for variables with long (for example, SST), and fast (for example, atmospheric) adjustment time scale. An additional focus of the analysis is how the predictability in the initialized coupled seasonal predictions compares with estimates based on the AMIP simulations. The results indicate that differences in the set up of AMIP simulations and coupled predictions, for example, representation of air-sea interactions, and evolution of forecast spread from initial conditions do not change fundamental conclusion about the seasonal predictability. A discussion of the analysis presented herein, and its implications for the use of AMIP simulations for climate attribution, and for time-slice experiments to provide regional information, is also included. (orig.)

  17. Bayesian flood forecasting methods: A review

    Science.gov (United States)

    Han, Shasha; Coulibaly, Paulin

    2017-08-01

    Over the past few decades, floods have been seen as one of the most common and largely distributed natural disasters in the world. If floods could be accurately forecasted in advance, then their negative impacts could be greatly minimized. It is widely recognized that quantification and reduction of uncertainty associated with the hydrologic forecast is of great importance for flood estimation and rational decision making. Bayesian forecasting system (BFS) offers an ideal theoretic framework for uncertainty quantification that can be developed for probabilistic flood forecasting via any deterministic hydrologic model. It provides suitable theoretical structure, empirically validated models and reasonable analytic-numerical computation method, and can be developed into various Bayesian forecasting approaches. This paper presents a comprehensive review on Bayesian forecasting approaches applied in flood forecasting from 1999 till now. The review starts with an overview of fundamentals of BFS and recent advances in BFS, followed with BFS application in river stage forecasting and real-time flood forecasting, then move to a critical analysis by evaluating advantages and limitations of Bayesian forecasting methods and other predictive uncertainty assessment approaches in flood forecasting, and finally discusses the future research direction in Bayesian flood forecasting. Results show that the Bayesian flood forecasting approach is an effective and advanced way for flood estimation, it considers all sources of uncertainties and produces a predictive distribution of the river stage, river discharge or runoff, thus gives more accurate and reliable flood forecasts. Some emerging Bayesian forecasting methods (e.g. ensemble Bayesian forecasting system, Bayesian multi-model combination) were shown to overcome limitations of single model or fixed model weight and effectively reduce predictive uncertainty. In recent years, various Bayesian flood forecasting approaches have been

  18. An artificial neural network model for rainfall forecasting in Bangkok, Thailand

    Directory of Open Access Journals (Sweden)

    N. Q. Hung

    2009-08-01

    Full Text Available This paper presents a new approach using an Artificial Neural Network technique to improve rainfall forecast performance. A real world case study was set up in Bangkok; 4 years of hourly data from 75 rain gauge stations in the area were used to develop the ANN model. The developed ANN model is being applied for real time rainfall forecasting and flood management in Bangkok, Thailand. Aimed at providing forecasts in a near real time schedule, different network types were tested with different kinds of input information. Preliminary tests showed that a generalized feedforward ANN model using hyperbolic tangent transfer function achieved the best generalization of rainfall. Especially, the use of a combination of meteorological parameters (relative humidity, air pressure, wet bulb temperature and cloudiness, the rainfall at the point of forecasting and rainfall at the surrounding stations, as an input data, advanced ANN model to apply with continuous data containing rainy and non-rainy period, allowed model to issue forecast at any moment. Additionally, forecasts by ANN model were compared to the convenient approach namely simple persistent method. Results show that ANN forecasts have superiority over the ones obtained by the persistent model. Rainfall forecasts for Bangkok from 1 to 3 h ahead were highly satisfactory. Sensitivity analysis indicated that the most important input parameter besides rainfall itself is the wet bulb temperature in forecasting rainfall.

  19. Multi-model forecast skill for mid-summer rainfall over southern Africa

    CSIR Research Space (South Africa)

    Landman, WA

    2012-02-01

    Full Text Available -model forecasts outperform the single 17 model forecasts, that the two multi-model schemes produce about equally skilful 18 forecasts, and that the forecasts perform better during El Ni?o and La Ni?a 19 seasons than during neutral years. 20 21 22 3 1... to be 19 anomalously dry during El Ni?o years and anomalously wet during La Ni?a years, 20 although wet El Ni?o seasons and dry La Ni?a seasons are not uncommon. 21 Indian and Atlantic Ocean SST also have a statistically detectable influence on 22 South...

  20. Forecasting the northern African dust outbreak towards Europe in April 2011: a model intercomparison

    Directory of Open Access Journals (Sweden)

    N. Huneeus

    2016-04-01

    Full Text Available In the framework of the World Meteorological Organisation's Sand and Dust Storm Warning Advisory and Assessment System, we evaluated the predictions of five state-of-the-art dust forecast models during an intense Saharan dust outbreak affecting western and northern Europe in April 2011. We assessed the capacity of the models to predict the evolution of the dust cloud with lead times of up to 72 h using observations of aerosol optical depth (AOD from the AErosol RObotic NETwork (AERONET and the Moderate Resolution Imaging Spectroradiometer (MODIS and dust surface concentrations from a ground-based measurement network. In addition, the predicted vertical dust distribution was evaluated with vertical extinction profiles from the Cloud and Aerosol Lidar with Orthogonal Polarization (CALIOP. To assess the diversity in forecast capability among the models, the analysis was extended to wind field (both surface and profile, synoptic conditions, emissions and deposition fluxes. Models predict the onset and evolution of the AOD for all analysed lead times. On average, differences among the models are larger than differences among lead times for each individual model. In spite of large differences in emission and deposition, the models present comparable skill for AOD. In general, models are better in predicting AOD than near-surface dust concentration over the Iberian Peninsula. Models tend to underestimate the long-range transport towards northern Europe. Our analysis suggests that this is partly due to difficulties in simulating the vertical distribution dust and horizontal wind. Differences in the size distribution and wet scavenging efficiency may also account for model diversity in long-range transport.

  1. Forecasting the northern African dust outbreak towards Europe in April 2011: a model intercomparison

    Science.gov (United States)

    Huneeus, N.; Basart, S.; Fiedler, S.; Morcrette, J.-J.; Benedetti, A.; Mulcahy, J.; Terradellas, E.; Pérez García-Pando, C.; Pejanovic, G.; Nickovic, S.; Arsenovic, P.; Schulz, M.; Cuevas, E.; Baldasano, J. M.; Pey, J.; Remy, S.; Cvetkovic, B.

    2016-04-01

    In the framework of the World Meteorological Organisation's Sand and Dust Storm Warning Advisory and Assessment System, we evaluated the predictions of five state-of-the-art dust forecast models during an intense Saharan dust outbreak affecting western and northern Europe in April 2011. We assessed the capacity of the models to predict the evolution of the dust cloud with lead times of up to 72 h using observations of aerosol optical depth (AOD) from the AErosol RObotic NETwork (AERONET) and the Moderate Resolution Imaging Spectroradiometer (MODIS) and dust surface concentrations from a ground-based measurement network. In addition, the predicted vertical dust distribution was evaluated with vertical extinction profiles from the Cloud and Aerosol Lidar with Orthogonal Polarization (CALIOP). To assess the diversity in forecast capability among the models, the analysis was extended to wind field (both surface and profile), synoptic conditions, emissions and deposition fluxes. Models predict the onset and evolution of the AOD for all analysed lead times. On average, differences among the models are larger than differences among lead times for each individual model. In spite of large differences in emission and deposition, the models present comparable skill for AOD. In general, models are better in predicting AOD than near-surface dust concentration over the Iberian Peninsula. Models tend to underestimate the long-range transport towards northern Europe. Our analysis suggests that this is partly due to difficulties in simulating the vertical distribution dust and horizontal wind. Differences in the size distribution and wet scavenging efficiency may also account for model diversity in long-range transport.

  2. Modelling self-optimised short term load forecasting for medium voltage loads using tunning fuzzy systems and Artificial Neural Networks

    International Nuclear Information System (INIS)

    Mahmoud, Thair S.; Habibi, Daryoush; Hassan, Mohammed Y.; Bass, Octavian

    2015-01-01

    Highlights: • A novel Short Term Medium Voltage (MV) Load Forecasting (STLF) model is presented. • A knowledge-based STLF error control mechanism is implemented. • An Artificial Neural Network (ANN)-based optimum tuning is applied on STLF. • The relationship between load profiles and operational conditions is analysed. - Abstract: This paper presents an intelligent mechanism for Short Term Load Forecasting (STLF) models, which allows self-adaptation with respect to the load operational conditions. Specifically, a knowledge-based FeedBack Tunning Fuzzy System (FBTFS) is proposed to instantaneously correlate the information about the demand profile and its operational conditions to make decisions for controlling the model’s forecasting error rate. To maintain minimum forecasting error under various operational scenarios, the FBTFS adaptation was optimised using a Multi-Layer Perceptron Artificial Neural Network (MLPANN), which was trained using Backpropagation algorithm, based on the information about the amount of error and the operational conditions at time of forecasting. For the sake of comparison and performance testing, this mechanism was added to the conventional forecasting methods, i.e. Nonlinear AutoRegressive eXogenous-Artificial Neural Network (NARXANN), Fuzzy Subtractive Clustering Method-based Adaptive Neuro Fuzzy Inference System (FSCMANFIS) and Gaussian-kernel Support Vector Machine (GSVM), and the measured forecasting error reduction average in a 12 month simulation period was 7.83%, 8.5% and 8.32% respectively. The 3.5 MW variable load profile of Edith Cowan University (ECU) in Joondalup, Australia, was used in the modelling and simulations of this model, and the data was provided by Western Power, the transmission and distribution company of the state of Western Australia.

  3. eWaterCycle: A global operational hydrological forecasting model

    Science.gov (United States)

    van de Giesen, Nick; Bierkens, Marc; Donchyts, Gennadii; Drost, Niels; Hut, Rolf; Sutanudjaja, Edwin

    2015-04-01

    Development of an operational hyper-resolution hydrological global model is a central goal of the eWaterCycle project (www.ewatercycle.org). This operational model includes ensemble forecasts (14 days) to predict water related stress around the globe. Assimilation of near-real time satellite data is part of the intended product that will be launched at EGU 2015. The challenges come from several directions. First, there are challenges that are mainly computer science oriented but have direct practical hydrological implications. For example, we aim to make use as much as possible of existing standards and open-source software. For example, different parts of our system are coupled through the Basic Model Interface (BMI) developed in the framework of the Community Surface Dynamics Modeling System (CSDMS). The PCR-GLOBWB model, built by Utrecht University, is the basic hydrological model that is the engine of the eWaterCycle project. Re-engineering of parts of the software was needed for it to run efficiently in a High Performance Computing (HPC) environment, and to be able to interface using BMI, and run on multiple compute nodes in parallel. The final aim is to have a spatial resolution of 1km x 1km, which is currently 10 x 10km. This high resolution is computationally not too demanding but very memory intensive. The memory bottleneck becomes especially apparent for data assimilation, for which we use OpenDA. OpenDa allows for different data assimilation techniques without the need to build these from scratch. We have developed a BMI adaptor for OpenDA, allowing OpenDA to use any BMI compatible model. To circumvent memory shortages which would result from standard applications of the Ensemble Kalman Filter, we have developed a variant that does not need to keep all ensemble members in working memory. At EGU, we will present this variant and how it fits well in HPC environments. An important step in the eWaterCycle project was the coupling between the hydrological and

  4. Modeling and forecasting of electrical power demands for capacity planning

    International Nuclear Information System (INIS)

    Al-Shobaki, Salman; Mohsen, Mousa

    2008-01-01

    This paper describes the development of forecasting models to predict future generation and electrical power consumption in Jordan. This is critical to production cost since power is generated by burning expensive imported oil. Currently, the National Electric Power Company (NEPCO) is using regression models that only accounts for trend dynamics in their planning of loads and demand levels. The models are simplistic and are based on generated energy historical levels. They produce results on yearly bases and do not account for monthly variability in demand levels. The paper presents two models, one based on the generated energy data and the other is based on the consumed energy data. The models account for trend, monthly seasonality, and cycle dynamics. Both models are compared to NEPCO's model and indicate that NEPCO is producing energy at levels higher than needed (5.25%) thus increasing the loss in generated energy. The developed models also show a 13% difference between the generated energy and the consumed energy that is lost due to transmission line and in-house consumption

  5. Modeling and forecasting of electrical power demands for capacity planning

    Energy Technology Data Exchange (ETDEWEB)

    Al-Shobaki, Salman [Department of Industrial Engineering, Hashemite University, Zarka 13115 (Jordan); Mohsen, Mousa [Department of Mechanical Engineering, Hashemite University, Zarka 13115 (Jordan)

    2008-11-15

    This paper describes the development of forecasting models to predict future generation and electrical power consumption in Jordan. This is critical to production cost since power is generated by burning expensive imported oil. Currently, the National Electric Power Company (NEPCO) is using regression models that only accounts for trend dynamics in their planning of loads and demand levels. The models are simplistic and are based on generated energy historical levels. They produce results on yearly bases and do not account for monthly variability in demand levels. The paper presents two models, one based on the generated energy data and the other is based on the consumed energy data. The models account for trend, monthly seasonality, and cycle dynamics. Both models are compared to NEPCO's model and indicate that NEPCO is producing energy at levels higher than needed (5.25%) thus increasing the loss in generated energy. The developed models also show a 13% difference between the generated energy and the consumed energy that is lost due to transmission line and in-house consumption. (author)

  6. Ensemble empirical model decomposition and neuro-fuzzy conjunction model for middle and long-term runoff forecast

    Science.gov (United States)

    Tan, Q.

    2017-12-01

    Forecasting the runoff over longer periods, such as months and years, is one of the important tasks for hydrologists and water resource managers to maximize the potential of the limited water. However, due to the nonlinear and nonstationary characteristic of the natural runoff, it is hard to forecast the middle and long-term runoff with a satisfactory accuracy. It has been proven that the forecast performance can be improved by using signal decomposition techniques to product more cleaner signals as model inputs. In this study, a new conjunction model (EEMD-neuro-fuzzy) with adaptive ability is proposed. The ensemble empirical model decomposition (EEMD) is used to decompose the runoff time series into several components, which are with different frequencies and more cleaner than the original time series. Then the neuro-fuzzy model is developed for each component. The final forecast results can be obtained by summing the outputs of all neuro-fuzzy models. Unlike the conventional forecast model, the decomposition and forecast models in this study are adjusted adaptively as long as new runoff information is added. The proposed models are applied to forecast the monthly runoff of Yichang station, located in Yangtze River of China. The results show that the performance of adaptive forecast model we proposed outperforms than the conventional forecast model, the Nash-Sutcliffe efficiency coefficient can reach to 0.9392. Due to its ability to process the nonstationary data, the forecast accuracy, especially in flood season, is improved significantly.

  7. Deep Learning Based Solar Flare Forecasting Model. I. Results for Line-of-sight Magnetograms

    Science.gov (United States)

    Huang, Xin; Wang, Huaning; Xu, Long; Liu, Jinfu; Li, Rong; Dai, Xinghua

    2018-03-01

    Solar flares originate from the release of the energy stored in the magnetic field of solar active regions, the triggering mechanism for these flares, however, remains unknown. For this reason, the conventional solar flare forecast is essentially based on the statistic relationship between solar flares and measures extracted from observational data. In the current work, the deep learning method is applied to set up the solar flare forecasting model, in which forecasting patterns can be learned from line-of-sight magnetograms of solar active regions. In order to obtain a large amount of observational data to train the forecasting model and test its performance, a data set is created from line-of-sight magnetogarms of active regions observed by SOHO/MDI and SDO/HMI from 1996 April to 2015 October and corresponding soft X-ray solar flares observed by GOES. The testing results of the forecasting model indicate that (1) the forecasting patterns can be automatically reached with the MDI data and they can also be applied to the HMI data; furthermore, these forecasting patterns are robust to the noise in the observational data; (2) the performance of the deep learning forecasting model is not sensitive to the given forecasting periods (6, 12, 24, or 48 hr); (3) the performance of the proposed forecasting model is comparable to that of the state-of-the-art flare forecasting models, even if the duration of the total magnetograms continuously spans 19.5 years. Case analyses demonstrate that the deep learning based solar flare forecasting model pays attention to areas with the magnetic polarity-inversion line or the strong magnetic field in magnetograms of active regions.

  8. Statistical models and time series forecasting of sulfur dioxide: a case study Tehran.

    Science.gov (United States)

    Hassanzadeh, S; Hosseinibalam, F; Alizadeh, R

    2009-08-01

    This study performed a time-series analysis, frequency distribution and prediction of SO(2) levels for five stations (Pardisan, Vila, Azadi, Gholhak and Bahman) in Tehran for the period of 2000-2005. Most sites show a quite similar characteristic with highest pollution in autumn-winter time and least pollution in spring-summer. The frequency distributions show higher peaks at two residential sites. The potential for SO(2) problems is high because of high emissions and the close geographical proximity of the major industrial and urban centers. The ACF and PACF are nonzero for several lags, indicating a mixed (ARMA) model, then at Bahman station an ARMA model was used for forecasting SO(2). The partial autocorrelations become close to 0 after about 5 lags while the autocorrelations remain strong through all the lags shown. The results proved that ARMA (2,2) model can provides reliable, satisfactory predictions for time series.

  9. OPTIMUM VOLUME OF BANK RESERVE: FORECASTING OF OVERDUE CREDIT INDEBTEDNESS USING COPULA MODELS

    Directory of Open Access Journals (Sweden)

    Kazakova K. A.

    2015-12-01

    Full Text Available The article propose to consider the possibility of RLUF-copulas application for the creation of joint distributions of overdue credit indebtedness ranks with macroeconomic indicators for the purpose of indebtedness forecasting and also for the definition of optimum volumes of reserve requirements for the corresponding losses. In this research the comparative analysis of multivariate distributions of RLUF-copula estimation with such classical copulas, as FGM-copula, Frank's copula and Gauss's copula is made. In the article the method of maximum likelihood is used for receiving estimates of model parameters. In case of RLUF-copula Bayesian estimates of parameters are received using the Metropolis algorithm with random volatility. Forecasting of bank reserve volumes for all received models is executed in the form of random sample generation by the means of the algorithm of acceptance-deviation for the creation of the corresponding sample of joint distribution using the copula density function. As the result of playing of hundred possible scenarios of indebtedness volumes is obtained the 95 % confidence level for the possible volume of credit indebtedness which can fully act as the optimum volume of reserve requirements for the corresponding credit losses.

  10. Insights from a history of seasonal inflow forecasting with a conceptual hydrologic model

    Science.gov (United States)

    Druce, Donald J.

    2001-08-01

    BC Hydro has used a conceptual hydrologic model for forecasting the seasonal inflows to its Mica project, on the Columbia River, for more than 20 years. The model estimates the snowpack on the forecast date using recently observed weather data and then calculates the runoff response to approximately 30 historical weather sequences over the remainder of the year. The ensemble of equally likely seasonal inflows makes up the forecast. Forecasts are issued as of the first of each month from January to August. The same model and modeller have made all of such forecasts for the Mica project. While some may see this as a failure to progress, others may see it as an unique opportunity to learn how well an older hydrologic model has performed, in practice, over the long term. Comments on both perspectives are offered, after the analyses of the forecasts have been presented. The forecasts have been analysed, as of each of the monthly forecast dates, to determine the accuracy of the mean and to establish any differences between ex post and ex ante measures of uncertainty. Results are then compared with those from a regression model that has also been used for forecasting the seasonal inflows to the Mica project over the same period of record.

  11. A stochastic HMM-based forecasting model for fuzzy time series.

    Science.gov (United States)

    Li, Sheng-Tun; Cheng, Yi-Chung

    2010-10-01

    Recently, fuzzy time series have attracted more academic attention than traditional time series due to their capability of dealing with the uncertainty and vagueness inherent in the data collected. The formulation of fuzzy relations is one of the key issues affecting forecasting results. Most of the present works adopt IF-THEN rules for relationship representation, which leads to higher computational overhead and rule redundancy. Sullivan and Woodall proposed a Markov-based formulation and a forecasting model to reduce computational overhead; however, its applicability is limited to handling one-factor problems. In this paper, we propose a novel forecasting model based on the hidden Markov model by enhancing Sullivan and Woodall's work to allow handling of two-factor forecasting problems. Moreover, in order to make the nature of conjecture and randomness of forecasting more realistic, the Monte Carlo method is adopted to estimate the outcome. To test the effectiveness of the resulting stochastic model, we conduct two experiments and compare the results with those from other models. The first experiment consists of forecasting the daily average temperature and cloud density in Taipei, Taiwan, and the second experiment is based on the Taiwan Weighted Stock Index by forecasting the exchange rate of the New Taiwan dollar against the U.S. dollar. In addition to improving forecasting accuracy, the proposed model adheres to the central limit theorem, and thus, the result statistically approximates to the real mean of the target value being forecast.

  12. A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method.

    Science.gov (United States)

    Yang, Jun-He; Cheng, Ching-Hsue; Chan, Chia-Pan

    2017-01-01

    Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir's water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir's water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.

  13. A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method

    Directory of Open Access Journals (Sweden)

    Jun-He Yang

    2017-01-01

    Full Text Available Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir’s water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir’s water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.

  14. 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.

  15. Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models.

    Science.gov (United States)

    Luo, Li; Luo, Le; Zhang, Xinli; He, Xiaoli

    2017-07-10

    Accurate forecasting of hospital outpatient visits is beneficial for the reasonable planning and allocation of healthcare resource to meet the medical demands. In terms of the multiple attributes of daily outpatient visits, such as randomness, cyclicity and trend, time series methods, ARIMA, can be a good choice for outpatient visits forecasting. On the other hand, the hospital outpatient visits are also affected by the doctors' scheduling and the effects are not pure random. Thinking about the impure specialty, this paper presents a new forecasting model that takes cyclicity and the day of the week effect into consideration. We formulate a seasonal ARIMA (SARIMA) model on a daily time series and then a single exponential smoothing (SES) model on the day of the week time series, and finally establish a combinatorial model by modifying them. The models are applied to 1 year of daily visits data of urban outpatients in two internal medicine departments of a large hospital in Chengdu, for forecasting the daily outpatient visits about 1 week ahead. The proposed model is applied to forecast the cross-sectional data for 7 consecutive days of daily outpatient visits over an 8-weeks period based on 43 weeks of observation data during 1 year. The results show that the two single traditional models and the combinatorial model are simplicity of implementation and low computational intensiveness, whilst being appropriate for short-term forecast horizons. Furthermore, the combinatorial model can capture the comprehensive features of the time series data better. Combinatorial model can achieve better prediction performance than the single model, with lower residuals variance and small mean of residual errors which needs to be optimized deeply on the next research step.

  16. Can models help to forecast rainwater dynamics for rainfed ecosystem?

    Directory of Open Access Journals (Sweden)

    Mukhtar Ahmed

    2014-10-01

    Full Text Available Simulation models are important tools to explore and illustrate dynamics of climatic variables in crop based ecosystem. In the rainfed ecosystem (RE, wheat production is impinged on certain climatic events per se high variability in rainfall and increased temperature. These climatic events turn out due to climatic drivers like Sea Surface Temperatures (SSTs and pressure. Current study is aimed to analyze long term rainfall data (1961–2011 of Pakistan׳s rainfed ecosystem zone (Islamabad, Chakwal and Talagang by using Agricultural Production Systems Simulator (APSIM and R model. The principal objective of this analysis was to study the link between SOI phases and SSTs; and thereby understanding the pattern of climate change due to these climatic drivers under rainfed conditions in Pakistan. The results revealed a positive link between July SOI phases and the rainfall variability during October–November (the sowing time of wheat in Pakistan. Long term rainfall data analysis (1961–2011 of Islamabad, Chakwal and Talagang revealed 44%, 40%, 35% possibility of exceeding median rainfall near zero whereas probability of consistently negative SOI phases were 35%, 34% and 33% respectively during July. Similarly, the forecasting results estimated by R using covariates like dry spell, NINO1.2, NINO3, NINO4, NINO3.4 and IOD of different months revealed that prediction of monsoon, wheat early growth, wheat grain filling period and total wheat growing season rainfall, have significant signals with climatic drivers. The study justified the importance of models in the decision making processes and rainfall forecasting as a beneficial and necessary tool for rainfed ecosystem conservation.

  17. Forecasting Italian seismicity through a spatio-temporal physical model: importance of considering time-dependency and reliability of the forecast

    Directory of Open Access Journals (Sweden)

    Amir Hakimhashemi

    2010-11-01

    Full Text Available We apply here a forecasting model to the Italian region for the spatio-temporal distribution of seismicity based on a smoothing Kernel function, Coulomb stress variations, and a rate-and-state friction law. We tested the feasibility of this approach, and analyzed the importance of introducing time-dependency in forecasting future events. The change in seismicity rate as a function of time was estimated by calculating the Coulomb stress change imparted by large earthquakes. We applied our approach to the region of Italy, and used all of the cataloged earthquakes that occurred up to 2006 to generate the reference seismicity rate. For calculation of the time-dependent seismicity rate changes, we estimated the rate-and-state stress transfer imparted by all of the ML≥4.0 earthquakes that occurred during 2007 and 2008. To validate the results, we first compared the reference seismicity rate with the distribution of ML≥1.8 earthquakes since 2007, using both a non-declustered and a declustered catalog. A positive correlation was found, and all of the forecast earthquakes had locations within 82% and 87% of the study area with the highest seismicity rate, respectively. Furthermore, 95% of the forecast earthquakes had locations within 27% and 47% of the study area with the highest seismicity rate, respectively. For the time-dependent seismicity rate changes, the number of events with locations in the regions with a seismicity rate increase was 11% more than in the regions with a seismicity rate decrease.

  18. Retrospective forecast of ETAS model with daily parameters estimate

    Science.gov (United States)

    Falcone, Giuseppe; Murru, Maura; Console, Rodolfo; Marzocchi, Warner; Zhuang, Jiancang

    2016-04-01

    We present a retrospective ETAS (Epidemic Type of Aftershock Sequence) model based on the daily updating of free parameters during the background, the learning and the test phase of a seismic sequence. The idea was born after the 2011 Tohoku-Oki earthquake. The CSEP (Collaboratory for the Study of Earthquake Predictability) Center in Japan provided an appropriate testing benchmark for the five 1-day submitted models. Of all the models, only one was able to successfully predict the number of events that really happened. This result was verified using both the real time and the revised catalogs. The main cause of the failure was in the underestimation of the forecasted events, due to model parameters maintained fixed during the test. Moreover, the absence in the learning catalog of an event similar to the magnitude of the mainshock (M9.0), which drastically changed the seismicity in the area, made the learning parameters not suitable to describe the real seismicity. As an example of this methodological development we show the evolution of the model parameters during the last two strong seismic sequences in Italy: the 2009 L'Aquila and the 2012 Reggio Emilia episodes. The achievement of the model with daily updated parameters is compared with that of same model where the parameters remain fixed during the test time.

  19. Model Forecast Skill and Sensitivity to Initial Conditions in the Seasonal Sea Ice Outlook

    Science.gov (United States)

    Blanchard-Wrigglesworth, E.; Cullather, R. I.; Wang, W.; Zhang, J.; Bitz, C. M.

    2015-01-01

    We explore the skill of predictions of September Arctic sea ice extent from dynamical models participating in the Sea Ice Outlook (SIO). Forecasts submitted in August, at roughly 2 month lead times, are skillful. However, skill is lower in forecasts submitted to SIO, which began in 2008, than in hindcasts (retrospective forecasts) of the last few decades. The multimodel mean SIO predictions offer slightly higher skill than the single-model SIO predictions, but neither beats a damped persistence forecast at longer than 2 month lead times. The models are largely unsuccessful at predicting each other, indicating a large difference in model physics and/or initial conditions. Motivated by this, we perform an initial condition sensitivity experiment with four SIO models, applying a fixed -1 m perturbation to the initial sea ice thickness. The significant range of the response among the models suggests that different model physics make a significant contribution to forecast uncertainty.

  20. Forecasting rain events - Meteorological models or collective intelligence?

    Science.gov (United States)

    Arazy, Ofer; Halfon, Noam; Malkinson, Dan

    2015-04-01

    Collective intelligence is shared (or group) intelligence that emerges from the collective efforts of many individuals. Collective intelligence is the aggregate of individual contributions: from simple collective decision making to more sophisticated aggregations such as in crowdsourcing and peer-production systems. In particular, collective intelligence could be used in making predictions about future events, for example by using prediction markets to forecast election results, stock prices, or the outcomes of sport events. To date, there is little research regarding the use of collective intelligence for prediction of weather forecasting. The objective of this study is to investigate the extent to which collective intelligence could be utilized to accurately predict weather events, and in particular rainfall. Our analyses employ metrics of group intelligence, as well as compare the accuracy of groups' predictions against the predictions of the standard model used by the National Meteorological Services. We report on preliminary results from a study conducted over the 2013-2014 and 2014-2015 winters. We have built a web site that allows people to make predictions on precipitation levels on certain locations. During each competition participants were allowed to enter their precipitation forecasts (i.e. 'bets') at three locations and these locations changed between competitions. A precipitation competition was defined as a 48-96 hour period (depending on the expected weather conditions), bets were open 24-48 hours prior to the competition, and during betting period participants were allowed to change their bets with no limitation. In order to explore the effect of transparency, betting mechanisms varied across study's sites: full transparency (participants able to see each other's bets); partial transparency (participants see the group's average bet); and no transparency (no information of others' bets is made available). Several interesting findings emerged from

  1. Use of medium-range numerical weather prediction model output to produce forecasts of streamflow

    Science.gov (United States)

    Clark, M.P.; Hay, L.E.

    2004-01-01

    This paper examines an archive containing over 40 years of 8-day atmospheric forecasts over the contiguous United States from the NCEP reanalysis project to assess the possibilities for using medium-range numerical weather prediction model output for predictions of streamflow. This analysis shows the biases in the NCEP forecasts to be quite extreme. In many regions, systematic precipitation biases exceed 100% of the mean, with temperature biases exceeding 3??C. In some locations, biases are even higher. The accuracy of NCEP precipitation and 2-m maximum temperature forecasts is computed by interpolating the NCEP model output for each forecast day to the location of each station in the NWS cooperative network and computing the correlation with station observations. Results show that the accuracy of the NCEP forecasts is rather low in many areas of the country. Most apparent is the generally low skill in precipitation forecasts (particularly in July) and low skill in temperature forecasts in the western United States, the eastern seaboard, and the southern tier of states. These results outline a clear need for additional processing of the NCEP Medium-Range Forecast Model (MRF) output before it is used for hydrologic predictions. Techniques of model output statistics (MOS) are used in this paper to downscale the NCEP forecasts to station locations. Forecasted atmospheric variables (e.g., total column precipitable water, 2-m air temperature) are used as predictors in a forward screening multiple linear regression model to improve forecasts of precipitation and temperature for stations in the National Weather Service cooperative network. This procedure effectively removes all systematic biases in the raw NCEP precipitation and temperature forecasts. MOS guidance also results in substantial improvements in the accuracy of maximum and minimum temperature forecasts throughout the country. For precipitation, forecast improvements were less impressive. MOS guidance increases

  2. Evaluating Forecasting Models for Unemployment Rates by Gender in Selected European Countries

    Directory of Open Access Journals (Sweden)

    Ksenija Dumičić

    2017-03-01

    Full Text Available The unemployment can be considered as one of the main economic problems. The aim of this article is to examine the differences in male and female unemployment rates in selected European countries and to predict their future trends by using different statistical forecasting models. Furthermore, the impact of adding a new data point on the selection of the most appropriate statistical forecasting model and on the overall forecasting errors values is also evaluated. Male and female unemployment rates are observed for twelve European countries in the period from 1991 to 2014. Four statistical forecasting models have been selected and applied and the most appropriate model is considered to be the one with the lowest overall forecasting errors values. The analysis has shown that in the period from 1991 to 2014 the decreasing trend of unemployment rates in the short-run is forecasted for more Eastern Balkan than the EU-28 countries. An additional data point for male and female unemployment rates in 2014 led to somewhat smaller forecasting errors in more than half of the observed countries. However, the additional data point does not necessarily improve forecasting performances of the used statistical forecasting models.

  3. Probabilistic Price Forecasting for Day-Ahead and Intraday Markets: Beyond the Statistical Model

    Directory of Open Access Journals (Sweden)

    José R. Andrade

    2017-10-01

    Full Text Available Forecasting the hourly spot price of day-ahead and intraday markets is particularly challenging in electric power systems characterized by high installed capacity of renewable energy technologies. In particular, periods with low and high price levels are difficult to predict due to a limited number of representative cases in the historical dataset, which leads to forecast bias problems and wide forecast intervals. Moreover, these markets also require the inclusion of multiple explanatory variables, which increases the complexity of the model without guaranteeing a forecasting skill improvement. This paper explores information from daily futures contract trading and forecast of the daily average spot price to correct point and probabilistic forecasting bias. It also shows that an adequate choice of explanatory variables and use of simple models like linear quantile regression can lead to highly accurate spot price point and probabilistic forecasts. In terms of point forecast, the mean absolute error was 3.03 €/MWh for day-ahead market and a maximum value of 2.53 €/MWh was obtained for intraday session 6. The probabilistic forecast results show sharp forecast intervals and deviations from perfect calibration below 7% for all market sessions.

  4. Electricity Demand Forecasting Using a Functional State Space Model

    OpenAIRE

    Nagbe , Komi; Cugliari , Jairo; Jacques , Julien

    2018-01-01

    In the last past years the liberalization of the electricity supply, the increase variability of electric appliances and their use, and the need to respond to the electricity demand in the real time had made electricity demand forecasting a challenge. To this challenge, many solutions are being proposed. The electricity demand involves many sources such as economic activities, household need and weather sources. All this sources make hard electricity demand forecasting. To forecast the electr...

  5. Econometric Forecasting Models for Air Traffic Passenger of Indonesia

    Directory of Open Access Journals (Sweden)

    Viktor Suryan

    2017-01-01

    Full Text Available One of the major benefits of the air transport services operating in bigger countries is the fact that they provide a vital social economic linkage. This study is an attempt to establish the determinants of the passenger air traffic in Indonesia. The main objective of the study is to determine the economic variables that affect the number of airline passengers using the econometrics model of projection with an emphasis on the use of panel data and to determine the economic variables that affect the number of airline passengers using the econometrics model of projection with an emphasis on the use of time series data. This research also predicts the upcoming number of air traffic passenger until 2030. Air transportation and the economic activity in a country are interdependent. This work first uses the data at the country level and then at the selected airport level for review. The methodology used in this study has adopted the study for both normal regression and panel data regression techniques. Once all these steps are performed, the final equation is taken up for the forecast of the passenger inflow data in the Indonesian airports. To forecast the same, the forecasted numbers of the GDP (Gross Domestic Product and population (independent variables were chosen as a part of the literature review exercise are used. The result of this study shows the GDP per capita have significant related to a number of passengers which the elasticity 2.23 (time-series data and 1.889 for panel data. The exchange rate variable is unrelated to a number of passengers as shown in the value of elasticity. In addition, the total of population gives small value for the elasticity. Moreover, the number of passengers is also affected by the dummy variable (deregulation. With three scenarios: low, medium and high for GDP per capita, the percentage of growth for total number of air traffic passenger from the year 2015 to 2030 is 199.3%, 205.7%, and 320.9% respectively.

  6. Modelling flow dynamics in water distribution networks using ...

    African Journals Online (AJOL)

    One such approach is the Artificial Neural Networks (ANNs) technique. The advantage of ANNs is that they are robust and can be used to model complex linear and non-linear systems without making implicit assumptions. ANNs can be trained to forecast flow dynamics in a water distribution network. Such flow dynamics ...

  7. A Novel Wind Speed Forecasting Model for Wind Farms of Northwest China

    Science.gov (United States)

    Wang, Jian-Zhou; Wang, Yun

    2017-01-01

    Wind resources are becoming increasingly significant due to their clean and renewable characteristics, and the integration of wind power into existing electricity systems is imminent. To maintain a stable power supply system that takes into account the stochastic nature of wind speed, accurate wind speed forecasting is pivotal. However, no single model can be applied to all cases. Recent studies show that wind speed forecasting errors are approximately 25% to 40% in Chinese wind farms. Presently, hybrid wind speed forecasting models are widely used and have been verified to perform better than conventional single forecasting models, not only in short-term wind speed forecasting but also in long-term forecasting. In this paper, a hybrid forecasting model is developed, the Similar Coefficient Sum (SCS) and Hermite Interpolation are exploited to process the original wind speed data, and the SVM model whose parameters are tuned by an artificial intelligence model is built to make forecast. The results of case studies show that the MAPE value of the hybrid model varies from 22.96% to 28.87 %, and the MAE value varies from 0.47 m/s to 1.30 m/s. Generally, Sign test, Wilcoxon's Signed-Rank test, and Morgan-Granger-Newbold test tell us that the proposed model is different from the compared models.

  8. Forecasting Foreign Institutional Investment Flows towards India Using ARIMA Modelling

    Directory of Open Access Journals (Sweden)

    Vaishali S. Dhingra

    2015-05-01

    Full Text Available India has witnessed substantial increase in capital flows, particularly Foreign Institutional Investment in equity as well as derivatives segment since the 1990s. However, FII flows are sighted as ‘hot money’- more volatile than other type of flows, which gets affected by the domestic and global- macro economic factors, thereby raising questions about the need to encourage FII flows in narrow and shallow (in terms of absorption capacity capital market such as India. This paper attempts to forecast daily Aggregate FII flow in Indian Capital market and particularly in Futures Market (Derivative Segment using Auto Regressive Integrated Moving Average (ARIMA model.The paper tries to examine FII flows in India towards futures market along with spot market by tracing which AR terms and/or MA terms influence the current inflow or outflow.

  9. Probabilistic short-term forecasting of eruption rate at Kīlauea Volcano using a physics-based model

    Science.gov (United States)

    Anderson, K. R.

    2016-12-01

    Deterministic models of volcanic eruptions yield predictions of future activity conditioned on uncertainty in the current state of the system. Physics-based eruption models are well-suited for deterministic forecasting as they can relate magma physics with a wide range of observations. Yet, physics-based eruption forecasting is strongly limited by an inadequate understanding of volcanic systems, and the need for eruption models to be computationally tractable. At Kīlauea Volcano, Hawaii, episodic depressurization-pressurization cycles of the magma system generate correlated, quasi-exponential variations in ground deformation and surface height of the active summit lava lake. Deflations are associated with reductions in eruption rate, or even brief eruptive pauses, and thus partly control lava flow advance rates and associated hazard. Because of the relatively well-understood nature of Kīlauea's shallow magma plumbing system, and because more than 600 of these events have been recorded to date, they offer a unique opportunity to refine a physics-based effusive eruption forecasting approach and apply it to lava eruption rates over short (hours to days) time periods. A simple physical model of the volcano ascribes observed data to temporary reductions in magma supply to an elastic reservoir filled with compressible magma. This model can be used to predict the evolution of an ongoing event, but because the mechanism that triggers events is unknown, event durations are modeled stochastically from previous observations. A Bayesian approach incorporates diverse data sets and prior information to simultaneously estimate uncertain model parameters and future states of the system. Forecasts take the form of probability distributions for eruption rate or cumulative erupted volume at some future time. Results demonstrate the significant uncertainties that still remain even for short-term eruption forecasting at a well-monitored volcano - but also the value of a physics

  10. A fuzzy inference model for short-term load forecasting

    International Nuclear Information System (INIS)

    Mamlook, Rustum; Badran, Omar; Abdulhadi, Emad

    2009-01-01

    This paper is concerned with the short-term load forecasting (STLF) in power system operations. It provides load prediction for generation scheduling and unit commitment decisions, and therefore precise load forecasting plays an important role in reducing the generation cost and the spinning reserve capacity. Short-term electricity demand forecasting (i.e., the prediction of hourly loads (demand)) is one of the most important tools by which an electric utility/company plans, dispatches the loading of generating units in order to meet system demand. The accuracy of the dispatching system, which is derived from the accuracy of the forecasting algorithm used, will determine the economics of the operation of the power system. The inaccuracy or large error in the forecast simply means that load matching is not optimized and consequently the generation and transmission systems are not being operated in an efficient manner. In the present study, a proposed methodology has been introduced to decrease the forecasted error and the processing time by using fuzzy logic controller on an hourly base. Therefore, it predicts the effect of different conditional parameters (i.e., weather, time, historical data, and random disturbances) on load forecasting in terms of fuzzy sets during the generation process. These parameters are chosen with respect to their priority and importance. The forecasted values obtained by fuzzy method were compared with the conventionally forecasted ones. The results showed that the STLF of the fuzzy implementation have more accuracy and better outcomes

  11. A system-theory-based model for monthly river runoff forecasting: model calibration and optimization

    Directory of Open Access Journals (Sweden)

    Wu Jianhua

    2014-03-01

    Full Text Available River runoff is not only a crucial part of the global water cycle, but it is also an important source for hydropower and an essential element of water balance. This study presents a system-theory-based model for river runoff forecasting taking the Hailiutu River as a case study. The forecasting model, designed for the Hailiutu watershed, was calibrated and verified by long-term precipitation observation data and groundwater exploitation data from the study area. Additionally, frequency analysis, taken as an optimization technique, was applied to improve prediction accuracy. Following model optimization, the overall relative prediction errors are below 10%. The system-theory-based prediction model is applicable to river runoff forecasting, and following optimization by frequency analysis, the prediction error is acceptable.

  12. Surrogate reservoir models for CSI well probabilistic production forecast

    Directory of Open Access Journals (Sweden)

    Saúl Buitrago

    2017-09-01

    Full Text Available The aim of this work is to present the construction and use of Surrogate Reservoir Models capable of accurately predicting cumulative oil production for every well stimulated with cyclic steam injection at any given time in a heavy oil reservoir in Mexico considering uncertain variables. The central composite experimental design technique was selected to capture the maximum amount of information from the model response with a minimum number of reservoir models simulations. Four input uncertain variables (the dead oil viscosity with temperature, the reservoir pressure, the reservoir permeability and oil sand thickness hydraulically connected to the well were selected as the ones with more impact on the initial hot oil production rate according to an analytical production prediction model. Twenty five runs were designed and performed with the STARS simulator for each well type on the reservoir model. The results show that the use of Surrogate Reservoir Models is a fast viable alternative to perform probabilistic production forecasting of the reservoir.

  13. Correlation Analysis of Water Demand and Predictive Variables for Short-Term Forecasting Models

    Directory of Open Access Journals (Sweden)

    B. M. Brentan

    2017-01-01

    Full Text Available Operational and economic aspects of water distribution make water demand forecasting paramount for water distribution systems (WDSs management. However, water demand introduces high levels of uncertainty in WDS hydraulic models. As a result, there is growing interest in developing accurate methodologies for water demand forecasting. Several mathematical models can serve this purpose. One crucial aspect is the use of suitable predictive variables. The most used predictive variables involve weather and social aspects. To improve the interrelation knowledge between water demand and various predictive variables, this study applies three algorithms, namely, classical Principal Component Analysis (PCA and machine learning powerful algorithms such as Self-Organizing Maps (SOMs and Random Forest (RF. We show that these last algorithms help corroborate the results found by PCA, while they are able to unveil hidden features for PCA, due to their ability to cope with nonlinearities. This paper presents a correlation study of three district metered areas (DMAs from Franca, a Brazilian city, exploring weather and social variables to improve the knowledge of residential demand for water. For the three DMAs, temperature, relative humidity, and hour of the day appear to be the most important predictive variables to build an accurate regression model.

  14. The importance of the reference populations for coherent mortality forecasting models

    DEFF Research Database (Denmark)

    Kjærgaard, Søren; Canudas-Romo, Vladimir; Vaupel, James W.

    -population mortality models aiming to find the optimal of the set of countries to use as reference population and analyse the importance of the selection of countries. The two multi-population mortality models used are the Li-Lee model and the Double-Gap life expectancy forecasting model. The reference populations......Coherent forecasting models that take into consideration mortality changes observed in different countries are today among the essential tools for demographers, actuaries and other researchers interested in forecasts. Medium and long term life expectancy forecasts are compared for two multi...... is calculated taking into account all the possible combinations of a set of 20 industrialized countries. The different reference populations possibilities are compared by their forecast performance. The results show that the selection of countries for multi-population mortality models has a significant effect...

  15. A multivariate time series approach to modeling and forecasting demand in the emergency department.

    Science.gov (United States)

    Jones, Spencer S; Evans, R Scott; Allen, Todd L; Thomas, Alun; Haug, Peter J; Welch, Shari J; Snow, Gregory L

    2009-02-01

    The goals of this investigation were to study the temporal relationships between the demands for key resources in the emergency department (ED) and the inpatient hospital, and to develop multivariate forecasting models. Hourly data were collected from three diverse hospitals for the year 2006. Descriptive analysis and model fitting were carried out using graphical and multivariate time series methods. Multivariate models were compared to a univariate benchmark model in terms of their ability to provide out-of-sample forecasts of ED census and the demands for diagnostic resources. Descriptive analyses revealed little temporal interaction between the demand for inpatient resources and the demand for ED resources at the facilities considered. Multivariate models provided more accurate forecasts of ED census and of the demands for diagnostic resources. Our results suggest that multivariate time series models can be used to reliably forecast ED patient census; however, forecasts of the demands for diagnostic resources were not sufficiently reliable to be useful in the clinical setting.

  16. Quantile Forecasting for Credit Risk Management Using Possibly Mis-specified Hidden Markov Models

    NARCIS (Netherlands)

    Banachewicz, K.P.; Lucas, A.

    2008-01-01

    Recent models for credit risk management make use of hidden Markov models (HMMs). HMMs are used to forecast quantiles of corporate default rates. Little research has been done on the quality of such forecasts if the underlying HMM is potentially misspecified. In this paper, we focus on

  17. Data Assimilation at FLUXNET to Improve Models towards Ecological Forecasting (Invited)

    Science.gov (United States)

    Luo, Y.

    2009-12-01

    Dramatically increased volumes of data from observational and experimental networks such as FLUXNET call for transformation of ecological research to increase its emphasis on quantitative forecasting. Ecological forecasting will also meet the societal need to develop better strategies for natural resource management in a world of ongoing global change. Traditionally, ecological forecasting has been based on process-based models, informed by data in largely ad hoc ways. Although most ecological models incorporate some representation of mechanistic processes, today’s ecological models are generally not adequate to quantify real-world dynamics and provide reliable forecasts with accompanying estimates of uncertainty. A key tool to improve ecological forecasting is data assimilation, which uses data to inform initial conditions and to help constrain a model during simulation to yield results that approximate reality as closely as possible. In an era with dramatically increased availability of data from observational and experimental networks, data assimilation is a key technique that helps convert the raw data into ecologically meaningful products so as to accelerate our understanding of ecological processes, test ecological theory, forecast changes in ecological services, and better serve the society. This talk will use examples to illustrate how data from FLUXNET have been assimilated with process-based models to improve estimates of model parameters and state variables; to quantify uncertainties in ecological forecasting arising from observations, models and their interactions; and to evaluate information contributions of data and model toward short- and long-term forecasting of ecosystem responses to global change.

  18. A Practical Model for Forecasting New Freshman Enrollment during the Application Period.

    Science.gov (United States)

    Paulsen, Michael B.

    1989-01-01

    A simple and effective model for forecasting freshman enrollment during the application period is presented step by step. The model requires minimal and readily available information, uses a simple linear regression analysis on a personal computer, and provides updated monthly forecasts. (MSE)

  19. Do regional weather models contribute to better wind power forecasts? A few Norwegian case studies

    DEFF Research Database (Denmark)

    Bremnes, John Bjørnar; Giebel, Gregor

    2017-01-01

    resolution of this grid determines how accurate meteorological processes can be modeled and thereby also limits forecast quality. In this study, two global and four regional operational NWP models with spatial horizontal resolutions ranging from 1 to 32 km were applied to make wind power forecasts up to 66...

  20. A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method

    OpenAIRE

    Jun-He Yang; Ching-Hsue Cheng; Chia-Pan Chan

    2017-01-01

    Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir's water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting m...