WorldWideScience

Sample records for modelled forecasts recovery

  1. Battlescale Forecast Model Sensitivity Study

    National Research Council Canada - National Science Library

    Sauter, Barbara

    2003-01-01

    .... Changes to the surface observations used in the Battlescale Forecast Model initialization led to no significant changes in the resulting forecast values of temperature, relative humidity, wind speed, or wind direction...

  2. 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.

  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. Macroeconomic models, forecasting, and policymaking

    OpenAIRE

    Pescatori, Andrea; Zaman, Saeed

    2011-01-01

    Models of the macroeconomy have gotten quite sophisticated, thanks to decades of development and advances in computing power. Such models have also become indispensable tools for monetary policymakers, useful both for forecasting and comparing different policy options. Their failure to predict the recent financial crisis does not negate their use, it only points to some areas that can be improved.

  5. On the Influence of Weather Forecast Errors in Short-Term Load Forecasting Models

    OpenAIRE

    Fay, D.; Ringwood, John; Condon, M.

    2004-01-01

    Weather information is an important factor in load forecasting models. This weather information usually takes the form of actual weather readings. However, online operation of load forecasting models requires the use of weather forecasts, with associated weather forecast errors. A technique is proposed to model weather forecast errors to reflect current accuracy. A load forecasting model is then proposed which combines the forecasts of several load forecasting models. This approach allows the...

  6. Operational models for forecasting Dst

    Science.gov (United States)

    Watanabe, S.; Sagawa, E.; Ohtaka, K.; Shimazu, H.

    We have constructed operational models for forecasting the geomagnetic storm index (Dst) two hours in advance from six parameters: the velocity and density of the solar wind, the magnitude of the interplanetary magnetic field (IMF), and the x, y, and z components of the IMF. Our models use an Elman-type neural network, and we forecast space weather by using real-time solar-wind data from the Advanced Composition Explorer spacecraft.The models have worked well since April of 1998 and the Dst values forecast using them have been made available to the public at http://www.crl.go.jp/uk/uk223/service/nnw/index.html. From February to October 1998 there were 11 storms with minimum Dst values below -80 nT, and for ten the difference between the forecast minimum Dst and the Dst calculated from data measured by ground stations was less than 23%.For the storm starting on 19 October, however, the difference was 40% because of the weak correlation between the ACE environment and the earth's environment during this event.The Dst depends on the orientation of the IMF relative to the solar magnetospheric x-y plane and seems to be relatively large when the y component of the IMF is positive and perhaps also when the x component is positive.

  7. Mesoscale model forecast verification during monsoon 2008

    Indian Academy of Sciences (India)

    The systematic error in the 850 hPa temperature indicates that largely the WRF model forecasts feature warm bias and the MM5 model forecasts feature cold bias. Features common to all the three models include warm bias over northwest India and cold bias over southeast peninsula. The 850 hPa specific humidity forecast ...

  8. 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...

  9. 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 ...

  10. 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 ...

  11. Forecasting Market Shares from Models for Sales

    NARCIS (Netherlands)

    D. Fok (Dennis); Ph.H.B.F. Franses (Philip Hans)

    2000-01-01

    textabstractDividing forecasts of brand sales by a forecast of category sales, when they are generated from brand specific sales-response models, renders biased forecasts of the brands' market shares. In this paper we therefore propose an easy-to-apply simulation-based method which results in

  12. Probabilistic Solar Forecasting Using Quantile Regression Models

    Directory of Open Access Journals (Sweden)

    Philippe Lauret

    2017-10-01

    Full Text Available In this work, we assess the performance of three probabilistic models for intra-day solar forecasting. More precisely, a linear quantile regression method is used to build three models for generating 1 h–6 h-ahead probabilistic forecasts. Our approach is applied to forecasting solar irradiance at a site experiencing highly variable sky conditions using the historical ground observations of solar irradiance as endogenous inputs and day-ahead forecasts as exogenous inputs. Day-ahead irradiance forecasts are obtained from the Integrated Forecast System (IFS, a Numerical Weather Prediction (NWP model maintained by the European Center for Medium-Range Weather Forecast (ECMWF. Several metrics, mainly originated from the weather forecasting community, are used to evaluate the performance of the probabilistic forecasts. The results demonstrated that the NWP exogenous inputs improve the quality of the intra-day probabilistic forecasts. The analysis considered two locations with very dissimilar solar variability. Comparison between the two locations highlighted that the statistical performance of the probabilistic models depends on the local sky conditions.

  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. 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. 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.

  16. Forecasting elections in Europe: Synthetic models

    Directory of Open Access Journals (Sweden)

    Michael S. Lewis-Beck

    2015-01-01

    Full Text Available Scientific work on national election forecasting has become most developed for the United States case, where three dominant approaches can be identified: Structuralists, Aggregators, and Synthesizers. For European cases, election forecasting models remain almost exclusively Structuralist. Here we join together structural modeling and aggregate polling results, to form a hybrid, which we label a Synthetic Model. This model contains a political economy core, to which poll numbers are added (to tap omitted variables. We apply this model to a sample of three Western European countries: Germany, Ireland, and the United Kingdom. This combinatory strategy appears to offer clear forecasting gains, in terms of lead and accuracy.

  17. 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…

  18. NAVO NCOM Relocatable Model: Fukushima Regional Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Preliminary NCOM Relocatable 1km forecast model for Fukushima Region. USERS ARE REMINDED TO USE THE FUKUSHIMA 1KM NCOM DATA WITH CAUTION. THE MODEL WAS INITIATED ON...

  19. 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

  20. 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.

  1. Description of the Battlescale Forecast Model

    National Research Council Canada - National Science Library

    Henmi, Teizi

    1998-01-01

    .... Army Integrated Meteorological System Block II software. The Battlescale Forecast Model can be used operationally over any part of the world by using meteorological data obtained through the Automated Weather Distribution System...

  2. 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

  3. 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.

  4. Econometric models for forecasting of macroeconomic indices

    OpenAIRE

    Sukhanova, E. I.; Shirnaeva, S. Y.; Mokronosov, A. 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 development in the past and their cause and effect interrelations. The aim of the article is to build econometric models for macroeconomic indices forec...

  5. 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

  6. 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)

  7. Forecasting with nonlinear time series models

    DEFF Research Database (Denmark)

    Kock, Anders Bredahl; Teräsvirta, Timo

    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......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...

  8. NEW CAR DEMAND MODELING AND FORECASTING USING BASS DIFFUSION MODEL

    OpenAIRE

    Zuhaimy Ismail; Noratikah Abu

    2013-01-01

    Forecasting model of new product demand has been developed and applied to forecast new vehicle demand in Malaysia. Since the publication of the Bass model in 1969, innovation of new diffusion theory has sparked considerable research among marketing science scholars, operational researchers and mathematicians. The building of Bass diffusion model for forecasting new product within the Malaysian society is presented in this study. The proposed model represents the spread level of new Proton car...

  9. 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...

  10. 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 ...

  11. Experts' adjustment to model-based forecasts: Does the forecast horizon matter?

    NARCIS (Netherlands)

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

    2007-01-01

    textabstractExperts may have domain-specific knowledge that is not included in a statistical model and that can improve forecasts. While one-step-ahead forecasts address the conditional mean of the variable, model-based forecasts for longer horizons have a tendency to convert to the unconditional

  12. Forecasting characteristic earthquakes in a minimalist model

    DEFF Research Database (Denmark)

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

    2003-01-01

    Using error diagrams, we quantify the forecasting of characteristic-earthquake occurence in a recently introduced minimalist model. Initially we connect the earthquake alarm at a fixed time after the occurence of a characteristic event. The evaluation of this strategy leads to a one...

  13. Mesoscale model forecast verification during monsoon 2008

    Indian Academy of Sciences (India)

    There have been very few mesoscale modelling studies of the Indian monsoon, with focus on the verification and intercomparison of the operational real time forecasts. With the exception of Das et al (2008), most of the studies in the literature are either the case studies of tropical cyclones and thunderstorms or the sensitivity ...

  14. Modeled Forecasts of Dengue Fever in San Juan, Puerto Rico Using NASA Satellite Enhanced Weather Forecasts

    Science.gov (United States)

    Morin, C.; Quattrochi, D. A.; Zavodsky, B.; Case, J.

    2015-12-01

    Dengue fever (DF) is an important mosquito transmitted disease that is strongly influenced by meteorological and environmental conditions. Recent research has focused on forecasting DF case numbers based on meteorological data. However, these forecasting tools have generally relied on empirical models that require long DF time series to train. Additionally, their accuracy has been tested retrospectively, using past meteorological data. Consequently, the operational utility of the forecasts are still in question because the error associated with weather and climate forecasts are not reflected in the results. Using up-to-date weekly dengue case numbers for model parameterization and weather forecast data as meteorological input, we produced weekly forecasts of DF cases in San Juan, Puerto Rico. Each week, the past weeks' case counts were used to re-parameterize a process-based DF model driven with updated weather forecast data to generate forecasts of DF case numbers. Real-time weather forecast data was produced using the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) system enhanced using additional high-resolution NASA satellite data. This methodology was conducted in a weekly iterative process with each DF forecast being evaluated using county-level DF cases reported by the Puerto Rico Department of Health. The one week DF forecasts were accurate especially considering the two sources of model error. First, weather forecasts were sometimes inaccurate and generally produced lower than observed temperatures. Second, the DF model was often overly influenced by the previous weeks DF case numbers, though this phenomenon could be lessened by increasing the number of simulations included in the forecast. Although these results are promising, we would like to develop a methodology to produce longer range forecasts so that public health workers can better prepare for dengue epidemics.

  15. Evaluation of the Mountain Wave Forecast Model's Stratospheric Turbulence Simulations

    National Research Council Canada - National Science Library

    Allen, Mark

    2003-01-01

    .... The Air Force Weather Agency (AFWA) requested a product with the capability of forecasting Stratoturb at 30, 50, and 70 mb using model data currently available, To facilitate their request, the Mountain Wave Forecast Model (MWFM...

  16. Generalized martingale model of the uncertainty evolution of streamflow forecasts

    Science.gov (United States)

    Zhao, Tongtiegang; Zhao, Jianshi; Yang, Dawen; Wang, Hao

    2013-07-01

    Streamflow forecasts are dynamically updated in real-time, thus facilitating a process of forecast uncertainty evolution. Forecast uncertainty generally decreases over time and as more hydrologic information becomes available. The process of forecasting and uncertainty updating can be described by the martingale model of forecast evolution (MMFE), which formulates the total forecast uncertainty of a streamflow in one future period as the sum of forecast improvements in the intermediate periods. This study tests the assumptions, i.e., unbiasedness, Gaussianity, temporal independence, and stationarity, of MMFE using real-world streamflow forecast data. The results show that (1) real-world forecasts can be biased and tend to underestimate the actual streamflow, and (2) real-world forecast uncertainty is non-Gaussian and heavy-tailed. Based on these statistical tests, this study proposes a generalized martingale model GMMFE for the simulation of biased and non-Gaussian forecast uncertainties. The new model combines the normal quantile transform (NQT) with MMFE to formulate the uncertainty evolution of real-world streamflow forecasts. Reservoir operations based on a synthetic forecast by GMMFE illustrates that applications of streamflow forecasting facilitate utility improvements and that special attention should be focused on the statistical distribution of forecast uncertainty.

  17. Heap Leaching: Modelling and Forecasting Using CFD Technology

    Directory of Open Access Journals (Sweden)

    Diane McBride

    2018-01-01

    Full Text Available Heap leach operations typically employ some form of modelling and forecasting tools to predict cash flow margins and project viability. However, these vary from simple spreadsheets to phenomenological models, with more complex models not commonly employed as they require the greatest amount of time and effort. Yet, accurate production modelling and forecasting are essential for managing production and potentially critical for successful operation of a complex heap, time and effort spent in setting up modelling tools initially may increase profitability in the long term. A brief overview of various modelling approaches is presented, but this paper focuses on the capabilities of a computational fluid dynamics (CFD model. Advances in computational capability allow for complex CFD models, coupled with leach kinetic models, to be applied to complex ore bodies. In this paper a comprehensive hydrodynamic CFD model is described and applied to chalcopyrite dissolution under heap operating conditions. The model is parameterized against experimental data and validated against a range of experimental leach tests under different thermal conditions. A three-dimensional ‘virtual’ heap, under fluctuating meteorological conditions, is simulated. Continuous and intermittent irrigation is investigated, showing copper recovery per unit volume of applied leach solution to be slightly increased for pulse irrigation.

  18. 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.

  19. 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...

  20. 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...

  1. 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...

  2. 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...

  3. 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.

  4. 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

  5. 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

  6. 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...

  7. 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

  8. Medium-term hydrologic forecasting in mountain basins using forecasting of a mesoscale numerical weather model

    Science.gov (United States)

    Castro Heredia, L. M.; Suarez, F. I.; Fernandez, B.; Maass, T.

    2016-12-01

    For forecasting of water resources, weather outputs provide a valuable source of information which is available online. Compared to traditional ground-based meteorological gauges, weather forecasts data offer spatially and temporally continuous data not yet evaluated and used in the forecasting of water resources in mountainous regions in Chile. Nevertheless, the use of this non-conventional data has been limited or null in developing countries, basically because of the spatial resolution, despite the high potential in the management of water resources. The adequate incorporation of these data in hydrological models requires its evaluation while taking into account the features of river basins in mountainous regions. This work presents an integrated forecasting system which represents a radical change in the way of making the streamflow forecasts in Chile, where the snowmelt forecast is the principal component of water resources management. The integrated system is composed of a physically based hydrological model, which is the prediction tool itself, together with a methodology for remote sensing data gathering that allows feed the hydrological model in real time, and meteorological forecasts from NCEP-CFSv2. Previous to incorporation of meteorological forecasts into the hydrological model, the weather outputs were evaluated and downscaling using statistical downscaling methods. The hydrological forecasts were evaluated in two mountain basins in Chile for a term of six months for the snowmelt period. In every month an assimilation process was performed, and the hydrological forecast was improved. Each month, the snow cover area (from remote sensing) and the streamflow observed were used to assimilate the model parameters in order to improve the next hydrological forecast using meteorological forecasts. The operation of the system in real time shows a good agreement between the streamflow and the snow cover area observed. The hydrological model and the weather

  9. 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...

  10. 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.

  11. Forecasting with nonlinear time series model: A Monte-Carlo ...

    African Journals Online (AJOL)

    In this paper, we propose a new method of forecasting with nonlinear time series model using Monte-Carlo Bootstrap method. This new method gives better result in terms of forecast root mean squared error (RMSE) when compared with the traditional Bootstrap method and Monte-Carlo method of forecasting using a ...

  12. 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.

  13. 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.

  14. Solid waste forecasting using modified ANFIS modeling.

    Science.gov (United States)

    Younes, Mohammad K; Nopiah, Z M; Basri, N E Ahmad; Basri, H; Abushammala, Mohammed F M; K N A, Maulud

    2015-10-01

    Solid waste prediction is crucial for sustainable solid waste management. Usually, accurate waste generation record is challenge in developing countries which complicates the modelling process. Solid waste generation is related to demographic, economic, and social factors. However, these factors are highly varied due to population and economy growths. The objective of this research is to determine the most influencing demographic and economic factors that affect solid waste generation using systematic approach, and then develop a model to forecast solid waste generation using a modified Adaptive Neural Inference System (MANFIS). The model evaluation was performed using Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and the coefficient of determination (R²). The results show that the best input variables are people age groups 0-14, 15-64, and people above 65 years, and the best model structure is 3 triangular fuzzy membership functions and 27 fuzzy rules. The model has been validated using testing data and the resulted training RMSE, MAE and R² were 0.2678, 0.045 and 0.99, respectively, while for testing phase RMSE =3.986, MAE = 0.673 and R² = 0.98. To date, a few attempts have been made to predict the annual solid waste generation in developing countries. This paper presents modeling of annual solid waste generation using Modified ANFIS, it is a systematic approach to search for the most influencing factors and then modify the ANFIS structure to simplify the model. The proposed method can be used to forecast the waste generation in such developing countries where accurate reliable data is not always available. Moreover, annual solid waste prediction is essential for sustainable planning.

  15. Modeling olive-crop forecasting in Tunisia

    Science.gov (United States)

    Ben Dhiab, Ali; Ben Mimoun, Mehdi; Oteros, Jose; Garcia-Mozo, Herminia; Domínguez-Vilches, Eugenio; Galán, Carmen; Abichou, Mounir; Msallem, Monji

    2017-05-01

    Tunisia is the world's second largest olive oil-producing region after the European Union. This paper reports on the use of models to forecast local olive crops, using data for Tunisia's five main olive-producing areas: Mornag, Jemmel, Menzel Mhiri, Chaal, and Zarzis. Airborne pollen counts were monitored over the period 1993-2011 using a Cour trap. Forecasting models were constructed using agricultural data (harvest size in tonnes of fruit/year) and data for several weather-related and phenoclimatic variables (rainfall, humidity, temperature, Growing Degree Days, and Chilling). Analysis of these data revealed that the amount of airborne pollen emitted over the pollen season as a whole (i.e., the Pollen Index) was the variable most influencing harvest size. Findings for all local models also indicated that the amount, timing, and distribution of rainfall (except during blooming) had a positive impact on final olive harvests. Air temperature also influenced final crop yield in three study provinces (Menzel Mhiri, Chaal, and Zarzis), but with varying consequences: in the model constructed for Chaal, cumulative maximum temperature from budbreak to start of flowering contributed positively to yield; in the Menzel Mhiri model, cumulative average temperatures during fruit development had a positive impact on output; in Zarzis, by contrast, cumulative maximum temperature during the period prior to flowering negatively influenced final crop yield. Data for agricultural and phenoclimatic variables can be used to construct valid models to predict annual variability in local olive-crop yields; here, models displayed an accuracy of 98, 93, 92, 91, and 88 % for Zarzis, Mornag, Jemmel, Chaal, and Menzel Mhiri, respectively.

  16. 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)

  17. Introducing Recovery Style for Modeling and Analyzing System Recovery

    NARCIS (Netherlands)

    Sözer, Hasan; Tekinerdogan, B.; Kruchten, P.; Garlan, D.; Woods, E.

    An analysis of the existing approaches for representing architectural views reveals that they focus mainly on functional concerns and are limited when considering quality concerns. We introduce the recovery style for modeling the structure of the system related to the recovery concern. The recovery

  18. 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.

  19. Development of Ensemble Model Based Water Demand Forecasting Model

    Science.gov (United States)

    Kwon, Hyun-Han; So, Byung-Jin; Kim, Seong-Hyeon; Kim, Byung-Seop

    2014-05-01

    In recent years, Smart Water Grid (SWG) concept has globally emerged over the last decade and also gained significant recognition in South Korea. Especially, there has been growing interest in water demand forecast and optimal pump operation and this has led to various studies regarding energy saving and improvement of water supply reliability. Existing water demand forecasting models are categorized into two groups in view of modeling and predicting their behavior in time series. One is to consider embedded patterns such as seasonality, periodicity and trends, and the other one is an autoregressive model that is using short memory Markovian processes (Emmanuel et al., 2012). The main disadvantage of the abovementioned model is that there is a limit to predictability of water demands of about sub-daily scale because the system is nonlinear. In this regard, this study aims to develop a nonlinear ensemble model for hourly water demand forecasting which allow us to estimate uncertainties across different model classes. The proposed model is consist of two parts. One is a multi-model scheme that is based on combination of independent prediction model. The other one is a cross validation scheme named Bagging approach introduced by Brieman (1996) to derive weighting factors corresponding to individual models. Individual forecasting models that used in this study are linear regression analysis model, polynomial regression, multivariate adaptive regression splines(MARS), SVM(support vector machine). The concepts are demonstrated through application to observed from water plant at several locations in the South Korea. Keywords: water demand, non-linear model, the ensemble forecasting model, uncertainty. Acknowledgements This subject is supported by Korea Ministry of Environment as "Projects for Developing Eco-Innovation Technologies (GT-11-G-02-001-6)

  20. 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)

  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. 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...

  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. Recovery capital pathways: Modelling the components of recovery wellbeing.

    Science.gov (United States)

    Cano, Ivan; Best, David; Edwards, Michael; Lehman, John

    2017-12-01

    In recent years, there has been recognition that recovery is a journey that involves the growth of recovery capital. Thus, recovery capital has become a commonly used term in addiction treatment and research yet its operationalization and measurement has been limited. Due to these limitations, there is little understanding of long-term recovery pathways and their clinical application. We used the data of 546 participants from eight different recovery residences spread across Florida, USA. We calculated internal consistency for recovery capital and wellbeing, then assessed their factor structure via confirmatory factor analysis. The relationships between time, recovery barriers and strengths, wellbeing and recovery capital, as well as the moderating effect of gender, were estimated using structural equations modelling. The proposed model obtained an acceptable fit (χ 2 (141, N=546)=533.642, pwellbeing. Gender differences were observed. We tested the pathways to recovery for residents in the recovery housing population. Our results have implications not only for retention as a predictor of sustained recovery and wellbeing but also for the importance of meaningful activities in promoting recovery capital and wellbeing. Copyright © 2017 Elsevier B.V. All rights reserved.

  6. Long Range River Discharge Forecasting Using the Gravity Recovery and Climate Experiment (GRACE) Satellite to Predict Conditions for Endemic Cholera

    Science.gov (United States)

    Jutla, A.; Akanda, A. S.; Colwell, R. R.

    2014-12-01

    Prediction of conditions of an impending disease outbreak remains a challenge but is achievable if the associated and appropriate large scale hydroclimatic process can be estimated in advance. Outbreaks of diarrheal diseases such as cholera, are related to episodic seasonal variability in river discharge in the regions where water and sanitation infrastructure are inadequate and insufficient. However, forecasting river discharge, few months in advance, remains elusive where cholera outbreaks are frequent, probably due to non-availability of geophysical data as well as transboundary water stresses. Here, we show that satellite derived water storage from Gravity Recovery and Climate Experiment Forecasting (GRACE) sensors can provide reliable estimates on river discharge atleast two months in advance over regional scales. Bayesian regression models predicted flooding and drought conditions, a prerequisite for cholera outbreaks, in Bengal Delta with an overall accuracy of 70% for upto 60 days in advance without using any other ancillary ground based data. Forecasting of river discharge will have significant impacts on planning and designing intervention strategies for potential cholera outbreaks in the coastal regions where the disease remain endemic and often fatal.

  7. Forecasting natural gas consumption in China by Bayesian Model Averaging

    Directory of Open Access Journals (Sweden)

    Wei Zhang

    2015-11-01

    Full Text Available With rapid growth of natural gas consumption in China, it is in urgent need of more accurate and reliable models to make a reasonable forecast. Considering the limitations of the single model and the model uncertainty, this paper presents a combinative method to forecast natural gas consumption by Bayesian Model Averaging (BMA. It can effectively handle the uncertainty associated with model structure and parameters, and thus improves the forecasting accuracy. This paper chooses six variables for forecasting the natural gas consumption, including GDP, urban population, energy consumption structure, industrial structure, energy efficiency and exports of goods and services. The results show that comparing to Gray prediction model, Linear regression model and Artificial neural networks, the BMA method provides a flexible tool to forecast natural gas consumption that will have a rapid growth in the future. This study can provide insightful information on natural gas consumption in the future.

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

    African Journals Online (AJOL)

    The forecasting model identified 8 key attributes for facilitation success based on performance measures from the 1999 Facilitator Customer Service Survey. During 2000 the annual Facilitator Customer Satisfaction Survey was employed to validate the findings of the forecasting model. A total of 1910 questionnaires were ...

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

    African Journals Online (AJOL)

    This study developed a neurofuzzy-based rainfall-runoff forecast model for river basin and evaluated the performance of the model. This was with a view to capturing the behaviour of hydrological and meterological variables involved in rainfall-runoff process to improve forecast accuracy of rainfallrunoff. Three hydrological ...

  11. Forecasting labour supply and population: An integrated stochastic model

    OpenAIRE

    Fuchs, Johann; Söhnlein, Doris; Weber, Brigitte; Weber, Enzo

    2017-01-01

    This paper presents a stochastic integrated model to forecast the German population and labour supply until 2060. Within a cohort-component approach, the population forecast applies principal components to birth, mortality, emigration and immigration rates. The labour force participation rates are estimated by means of an econometric time series approach. All time series are forecast by bootstrapping. This allows fully integrated simulations and the possibility to illustrate the uncertainties...

  12. Honolulu, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

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

  13. King Cove, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

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

  14. 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...

  15. 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...

  16. 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....

  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. 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...

  19. 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...

  20. 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...

  1. 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...

  2. 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....

  3. 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...

  4. 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....

  5. 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...

  6. 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...

  7. Mayaguez, Puerto Rico Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

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

  8. 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...

  9. 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....

  10. 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...

  11. 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...

  12. 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...

  13. 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....

  14. 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....

  15. 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...

  16. 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...

  17. 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....

  18. San Diego, California Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

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

  19. 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...

  20. Portland, Maine Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

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

  1. 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...

  2. 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...

  3. 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....

  4. 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....

  5. 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....

  6. 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....

  7. 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....

  8. 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....

  9. 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...

  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. 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...

  12. On the clustering of climate models in ensemble seasonal forecasting

    Science.gov (United States)

    Yuan, Xing; Wood, Eric F.

    2012-09-01

    Multi-model ensemble seasonal forecasting system has expanded in recent years, with a dozen coupled climate models around the world being used to produce hindcasts or real-time forecasts. However, many models are sharing similar atmospheric or oceanic components which may result in similar forecasts. This raises questions of whether the ensemble is over-confident if we treat each model equally, or whether we can obtain an effective subset of models that can retain predictability and skill as well. In this study, we use a hierarchical clustering method based on inverse trigonometric cosine function of the anomaly correlation of pairwise model hindcasts to measure the similarities among twelve American and European seasonal forecast models. Though similarities are found between models sharing the same atmospheric component, different versions of models from the same center sometimes produce quite different temperature forecasts, which indicate that detailed physics packages such as radiation and land surface schemes need to be analyzed in interpreting the clustering result. Uncertainties in clustering for different forecast lead times also make reducing redundant models more complicated. Predictability analysis shows that multi-model ensemble is not necessarily better than a single model, while the cluster ensemble shows consistent improvement against individual models. The eight model-based cluster ensemble forecast shows comparable performance to the total twelve model ensemble in terms of probabilistic forecast skill for accuracy and discrimination. This study also manifests that models developed in U.S. and Europe are more independent from each other, suggesting the necessity of international collaboration in enhancing multi-model ensemble seasonal forecasting.

  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. 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

  15. Precipitation forecasts and their uncertainty as input into hydrological models

    Directory of Open Access Journals (Sweden)

    M. Kobold

    2005-01-01

    Full Text Available Torrential streams and fast runoff are characteristic of most Slovenian rivers and extensive damage is caused almost every year by rainstorms affecting different regions of Slovenia. Rainfall-runoff models which are tools for runoff calculation can be used for flood forecasting. In Slovenia, the lag time between rainfall and runoff is only a few hours and on-line data are used only for now-casting. Predicted precipitation is necessary in flood forecasting some days ahead. The ECMWF (European Centre for Medium-Range Weather Forecasts model gives general forecasts several days ahead while more detailed precipitation data with the ALADIN/SI model are available for two days ahead. Combining the weather forecasts with the information on catchment conditions and a hydrological forecasting model can give advance warning of potential flooding notwithstanding a certain degree of uncertainty in using precipitation forecasts based on meteorological models. Analysis of the sensitivity of the hydrological model to the rainfall error has shown that the deviation in runoff is much larger than the rainfall deviation. Therefore, verification of predicted precipitation for large precipitation events was performed with the ECMWF model. Measured precipitation data were interpolated on a regular grid and compared with the results from the ECMWF model. The deviation in predicted precipitation from interpolated measurements is shown with the model bias resulting from the inability of the model to predict the precipitation correctly and a bias for horizontal resolution of the model and natural variability of precipitation.

  16. Forecasting German Car Sales Using Google Data and Multivariate Models

    OpenAIRE

    Fantazzini, Dean; Toktamysova, Zhamal

    2015-01-01

    Long-term forecasts are of key importance for the car industry due to the lengthy period of time required for the development and production processes. With this in mind, this paper proposes new multivariate models to forecast monthly car sales data using economic variables and Google online search data. An out-of-sample forecasting comparison with forecast horizons up to 2 years ahead was implemented using the monthly sales of ten car brands in Germany for the period from 2001M1 to 2014M6. M...

  17. 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......, 28 and 34 from all preceding days and provide our interpretation of the results. Results indicate that the dynamic interconnection between environmental conditions and broiler growth can be captured by the model. Furthermore, we found that a comparable forecast can be obtained by using input data...

  18. A numerical forecast model for road meteorology

    Science.gov (United States)

    Meng, Chunlei

    2017-05-01

    A fine-scale numerical model for road surface parameters prediction (BJ-ROME) is developed based on the Common Land Model. The model is validated using in situ observation data measured by the ROSA road weather stations of Vaisala Company, Finland. BJ-ROME not only takes into account road surface factors, such as imperviousness, relatively low albedo, high heat capacity, and high heat conductivity, but also considers the influence of urban anthropogenic heat, impervious surface evaporation, and urban land-use/land-cover changes. The forecast time span and the update interval of BJ-ROME in vocational operation are 24 and 3 h, respectively. The validation results indicate that BJ-ROME can successfully simulate the diurnal variation of road surface temperature both under clear-sky and rainfall conditions. BJ-ROME can simulate road water and snow depth well if the artificial removing was considered. Road surface energy balance in rainy days is quite different from that in clear-sky conditions. Road evaporation could not be neglected in road surface water cycle research. The results of sensitivity analysis show solar radiation correction coefficient, asphalt depth, and asphalt heat conductivity are important parameters in road interface temperatures simulation. The prediction results could be used as a reference of maintenance decision support system to mitigate the traffic jam and urban water logging especially in large cities.

  19. 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, P.; Jiang, L. P.; Rytter, N. G. M.

    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 is pr...

  20. 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.

  1. Forecasting project schedule performance using probabilistic and deterministic models

    Directory of Open Access Journals (Sweden)

    S.A. Abdel Azeem

    2014-04-01

    Full Text Available Earned value management (EVM was originally developed for cost management and has not widely been used for forecasting project duration. In addition, EVM based formulas for cost or schedule forecasting are still deterministic and do not provide any information about the range of possible outcomes and the probability of meeting the project objectives. The objective of this paper is to develop three models to forecast the estimated duration at completion. Two of these models are deterministic; earned value (EV and earned schedule (ES models. The third model is a probabilistic model and developed based on Kalman filter algorithm and earned schedule management. Hence, the accuracies of the EV, ES and Kalman Filter Forecasting Model (KFFM through the different project periods will be assessed and compared with the other forecasting methods such as the Critical Path Method (CPM, which makes the time forecast at activity level by revising the actual reporting data for each activity at a certain data date. A case study project is used to validate the results of the three models. Hence, the best model is selected based on the lowest average percentage of error. The results showed that the KFFM developed in this study provides probabilistic prediction bounds of project duration at completion and can be applied through the different project periods with smaller errors than those observed in EV and ES forecasting models.

  2. 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.

  3. Combined Forecasts from Linear and Nonlinear Time Series Models

    NARCIS (Netherlands)

    N. Terui (Nobuhiko); H.K. van Dijk (Herman)

    1999-01-01

    textabstractCombined forecasts from a linear and a nonlinear model are investigated for time series with possibly nonlinear characteristics. The forecasts are combined by a constant coefficient regression method as well as a time varying method. The time varying method allows for a locally

  4. Combined forecasts from linear and nonlinear time series models

    NARCIS (Netherlands)

    N. Terui (Nobuhiko); H.K. van Dijk (Herman)

    1999-01-01

    textabstractCombined forecasts from a linear and a nonlinear model are investigated for time series with possibly nonlinear characteristics. The forecasts are combined by a constant coefficient regression method as well as a time varying method. The time varying method allows for a locally

  5. 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.

  6. 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

  7. 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...

  8. 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)...

  9. 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)...

  10. 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)...

  11. A simulation model for forecasting downhill ski participation

    Science.gov (United States)

    Daniel J. Stynes; Daniel M. Spotts

    1980-01-01

    The purpose of this paper is to describe progress in the development of a general computer simulation model to forecast future levels of outdoor recreation participation. The model is applied and tested for downhill skiing in Michigan.

  12. Craig, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

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

  13. 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...

  14. 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)...

  15. 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)...

  16. Atka, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

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

  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. 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)...

  19. 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...

  20. 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...

  1. 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)...

  2. 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...

  3. 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)...

  4. 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)...

  5. 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)...

  6. 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)...

  7. 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)...

  8. 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...

  9. 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

  10. 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.

  11. Dynamic Hybrid Model for Short-Term Electricity Price Forecasting

    Directory of Open Access Journals (Sweden)

    Marin Cerjan

    2014-05-01

    Full Text Available Accurate forecasting tools are essential in the operation of electric power systems, especially in deregulated electricity markets. Electricity price forecasting is necessary for all market participants to optimize their portfolios. In this paper we propose a hybrid method approach for short-term hourly electricity price forecasting. The paper combines statistical techniques for pre-processing of data and a multi-layer (MLP neural network for forecasting electricity price and price spike detection. Based on statistical analysis, days are arranged into several categories. Similar days are examined by correlation significance of the historical data. Factors impacting the electricity price forecasting, including historical price factors, load factors and wind production factors are discussed. A price spike index (CWI is defined for spike detection and forecasting. Using proposed approach we created several forecasting models of diverse model complexity. The method is validated using the European Energy Exchange (EEX electricity price data records. Finally, results are discussed with respect to price volatility, with emphasis on the price forecasting accuracy.

  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...

  13. 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.

  14. 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.

  15. 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)

  16. 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

  17. 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

  18. Forecasting Models for Hydropower Unit Stability Using LS-SVM

    Directory of Open Access Journals (Sweden)

    Liangliang Qiao

    2015-01-01

    Full Text Available This paper discusses a least square support vector machine (LS-SVM approach for forecasting stability parameters of Francis turbine unit. To achieve training and testing data for the models, four field tests were presented, especially for the vibration in Y-direction of lower generator bearing (LGB and pressure in draft tube (DT. A heuristic method such as a neural network using Backpropagation (NNBP is introduced as a comparison model to examine the feasibility of forecasting performance. In the experimental results, LS-SVM showed superior forecasting accuracies and performances to the NNBP, which is of significant importance to better monitor the unit safety and potential faults diagnosis.

  19. The management of subsurface uncertainty using probabilistic modeling of life cycle production forecasts and cash flows

    International Nuclear Information System (INIS)

    Olatunbosun, O. O.

    1998-01-01

    The subject pertains to the implementation of the full range of subsurface uncertainties in life cycle probabilistic forecasting and its extension to project cash flows using the methodology of probabilities. A new tool has been developed in the probabilistic application of Crystal-Ball which can model reservoir volumetrics, life cycle production forecasts and project cash flows in a single environment. The tool is modular such that the volumetrics and cash flow modules are optional. Production forecasts are often generated by applying a decline equation to single best estimate values of input parameters such as initial potential, decline rate, abandonment rate etc -or sometimes by results of reservoir simulation. This new tool provides a means of implementing the full range of uncertainties and interdependencies of the input parameters into the production forecasts by defining the input parameters as probability density functions, PDFs and performing several iterations to generate an expectation curve forecast. Abandonment rate is implemented in each iteration via a link to an OPEX model. The expectation curve forecast is input into a cash flow model to generate a probabilistic NPV. Base case and sensitivity runs from reservoir simulation can likewise form the basis for a probabilistic production forecast from which a probabilistic cash flow can be generated. A good illustration of the application of this tool is in the modelling of the production forecast for a well that encounters its target reservoirs in OUT/ODT situation and thus has significant uncertainties. The uncertainty in presence and size (if present) of gas cap and dependency between ultimate recovery and initial potential amongst other uncertainties can be easily implemented in the production forecast with this tool. From the expectation curve forecast, a probabilistic NPV can be easily generated. Possible applications of this tool include: i. estimation of range of actual recoverable volumes based

  20. 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...

  1. 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...

  2. 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

  3. 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.

  4. Are traditional forecasting models suitable for hotels in Italian cities?

    OpenAIRE

    ELLERO, Andrea; PELLEGRINI, Paola

    2014-01-01

    The aim of this paper is to assess the performance of different widely-adopted models to forecast Italian hotel occupancy. In particular, the paper tests the different models for forecasting the demand in hotels located in urban areas, which typically experience both business and leisure demand, and whose demand is often affected by the presence of special events in the hotels themselves, or in their neighborhood.

  5. 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

  6. 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.

  7. Spatio-temporal modeling for real-time ozone forecasting.

    Science.gov (United States)

    Paci, Lucia; Gelfand, Alan E; Holland, David M

    2013-05-01

    The accurate assessment of exposure to ambient ozone concentrations is important for informing the public and pollution monitoring agencies about ozone levels that may lead to adverse health effects. High-resolution air quality information can offer significant health benefits by leading to improved environmental decisions. A practical challenge facing the U.S. Environmental Protection Agency (USEPA) is to provide real-time forecasting of current 8-hour average ozone exposure over the entire conterminous United States. Such real-time forecasting is now provided as spatial forecast maps of current 8-hour average ozone defined as the average of the previous four hours, current hour, and predictions for the next three hours. Current 8-hour average patterns are updated hourly throughout the day on the EPA-AIRNow web site. The contribution here is to show how we can substantially improve upon current real-time forecasting systems. To enable such forecasting, we introduce a downscaler fusion model based on first differences of real-time monitoring data and numerical model output. The model has a flexible coefficient structure and uses an efficient computational strategy to fit model parameters. Our hybrid computational strategy blends continuous background updated model fitting with real-time predictions. Model validation analyses show that we are achieving very accurate and precise ozone forecasts.

  8. 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

  9. 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.

  10. Short-Termed Integrated Forecasting System: 1993 Model documentation report

    Energy Technology Data Exchange (ETDEWEB)

    1993-05-01

    The purpose of this report is to define the Short-Term Integrated Forecasting System (STIFS) and describe its basic properties. The Energy Information Administration (EIA) of the US Energy Department (DOE) developed the STIFS model to generate short-term (up to 8 quarters), monthly forecasts of US supplies, demands, imports exports, stocks, and prices of various forms of energy. The models that constitute STIFS generate forecasts for a wide range of possible scenarios, including the following ones done routinely on a quarterly basis: A base (mid) world oil price and medium economic growth. A low world oil price and high economic growth. A high world oil price and low economic growth. This report is written for persons who want to know how short-term energy markets forecasts are produced by EIA. The report is intended as a reference document for model analysts, users, and the public.

  11. Comparison of various models on cancer rate and forecasting ...

    African Journals Online (AJOL)

    In this research work, three models were identified; linear regression model, exponential growth model and the quadratic trend model and the results of the work compared. Data collected from Niger State Hospital Management Board was used for the forecast and the result revealed that the quadratic trend model gave the ...

  12. 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.

  13. 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

  14. 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.

  15. A Modeling Framework for Improved Agricultural Water Supply Forecasting

    Science.gov (United States)

    Leavesley, G. H.; David, O.; Garen, D. C.; Lea, J.; Marron, J. K.; Pagano, T. C.; Perkins, T. R.; Strobel, M. L.

    2008-12-01

    The National Water and Climate Center (NWCC) of the USDA Natural Resources Conservation Service is moving to augment seasonal, regression-equation based water supply forecasts with distributed-parameter, physical process models enabling daily, weekly, and seasonal forecasting using an Ensemble Streamflow Prediction (ESP) methodology. This effort involves the development and implementation of a modeling framework, and associated models and tools, to provide timely forecasts for use by the agricultural community in the western United States where snowmelt is a major source of water supply. The framework selected to support this integration is the USDA Object Modeling System (OMS). OMS is a Java-based modular modeling framework for model development, testing, and deployment. It consists of a library of stand-alone science, control, and database components (modules), and a means to assemble selected components into a modeling package that is customized to the problem, data constraints, and scale of application. The framework is supported by utility modules that provide a variety of data management, land unit delineation and parameterization, sensitivity analysis, calibration, statistical analysis, and visualization capabilities. OMS uses an open source software approach to enable all members of the scientific community to collaboratively work on addressing the many complex issues associated with the design, development, and application of distributed hydrological and environmental models. A long-term goal in the development of these water-supply forecasting capabilities is the implementation of an ensemble modeling approach. This would provide forecasts using the results of multiple hydrologic models run on each basin.

  16. Forecasting regional house price inflation: a comparison between dynamic factor models and vector autoregressive models

    CSIR Research Space (South Africa)

    Das, Sonali

    2010-01-01

    Full Text Available This paper uses the dynamic factor model framework, which accommodates a large cross-section of macroeconomic time series, for forecasting regional house price inflation. In this study, the authors forecast house price inflation for five...

  17. 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.

  18. Short-Term Forecasting Models for Photovoltaic Plants: Analytical versus Soft-Computing Techniques

    OpenAIRE

    Monteiro, Claudio; Fernandez-Jimenez, L. Alfredo; Ramirez-Rosado, Ignacio J.; Muñoz-Jimenez, Andres; Lara-Santillan, Pedro M.

    2013-01-01

    We present and compare two short-term statistical forecasting models for hourly average electric power production forecasts of photovoltaic (PV) plants: the analytical PV power forecasting model (APVF) and the multiplayer perceptron PV forecasting model (MPVF). Both models use forecasts from numerical weather prediction (NWP) tools at the location of the PV plant as well as the past recorded values of PV hourly electric power production. The APVF model consists of an original modeling for adj...

  19. Aerosol Radiative Forcing and Weather Forecasts in the ECMWF Model

    Science.gov (United States)

    Bozzo, A.; Benedetti, A.; Rodwell, M. J.; Bechtold, P.; Remy, S.

    2015-12-01

    Aerosols play an important role in the energy balance of the Earth system via direct scattering and absorpiton of short-wave and long-wave radiation and indirect interaction with clouds. Diabatic heating or cooling by aerosols can also modify the vertical stability of the atmosphere and influence weather pattern with potential impact on the skill of global weather prediction models. The Copernicus Atmosphere Monitoring Service (CAMS) provides operational daily analysis and forecast of aerosol optical depth (AOD) for five aerosol species using a prognostic model which is part of the Integrated Forecasting System of the European Centre for Medium-Range Weather Forecasts (ECMWF-IFS). The aerosol component was developed during the research project Monitoring Atmospheric Composition and Climate (MACC). Aerosols can have a large impact on the weather forecasts in case of large aerosol concentrations as found during dust storms or strong pollution events. However, due to its computational burden, prognostic aerosols are not yet feasible in the ECMWF operational weather forecasts, and monthly-mean climatological fields are used instead. We revised the aerosol climatology used in the operational ECMWF IFS with one derived from the MACC reanalysis. We analyse the impact of changes in the aerosol radiative effect on the mean model climate and in medium-range weather forecasts, also in comparison with prognostic aerosol fields. The new climatology differs from the previous one by Tegen et al 1997, both in the spatial distribution of the total AOD and the optical properties of each aerosol species. The radiative impact of these changes affects the model mean bias at various spatial and temporal scales. On one hand we report small impacts on measures of large-scale forecast skill but on the other hand details of the regional distribution of aerosol concentration have a large local impact. This is the case for the northern Indian Ocean where the radiative impact of the mineral

  20. FORECASTING BY ECONOMETRIC MODELS AS SUPPORT TO MANAGEMENT

    OpenAIRE

    TINDE DOBRODOLAC

    2011-01-01

    In the contemporary environment characterized by the dynamic structure of factors and the unpredictability of the relations existing between them, the central problem is the selection of strategic goals. Forecasting is the necessary precursor to the planning process and includes research into the future course of events. Numerous methods and techniques of forecasting are used nowadays. Econometric models can be used successfully for predicting the future development of a phenomenon, and there...

  1. Comparison of various models on cancer rate and forecasting ...

    African Journals Online (AJOL)

    ADOWIE PERE

    ABSTRACT: In this research work, three models were identified; linear regression model, exponential growth model and the quadratic trend model and the results of the work compared. Data collected from Niger State Hospital. Management Board was used for the forecast and the result revealed that the quadratic trend ...

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

    African Journals Online (AJOL)

    PUBLICATIONS1

    with nonlinear time series model by comparing the RMSE with the traditional bootstrap and. Monte-Carlo method of forecasting. We use the logistic smooth transition autoregressive. (LSTAR) model as a case study. We first consider a linear model called the AR. (p) model of order p which satisfies the follow- ing linear ...

  3. Optimization of multi-model ensemble forecasting of typhoon waves

    Directory of Open Access Journals (Sweden)

    Shun-qi Pan

    2016-01-01

    Full Text Available Accurately forecasting ocean waves during typhoon events is extremely important in aiding the mitigation and minimization of their potential damage to the coastal infrastructure, and the protection of coastal communities. However, due to the complex hydrological and meteorological interaction and uncertainties arising from different modeling systems, quantifying the uncertainties and improving the forecasting accuracy of modeled typhoon-induced waves remain challenging. This paper presents a practical approach to optimizing model-ensemble wave heights in an attempt to improve the accuracy of real-time typhoon wave forecasting. A locally weighted learning algorithm is used to obtain the weights for the wave heights computed by the WAVEWATCH III wave model driven by winds from four different weather models (model-ensembles. The optimized weights are subsequently used to calculate the resulting wave heights from the model-ensembles. The results show that the Optimization is capable of capturing the different behavioral effects of the different weather models on wave generation. Comparison with the measurements at the selected wave buoy locations shows that the optimized weights, obtained through a training process, can significantly improve the accuracy of the forecasted wave heights over the standard mean values, particularly for typhoon-induced peak waves. The results also indicate that the algorithm is easy to implement and practical for real-time wave forecasting.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. Earthquake forecast models for Italy based on the RI algorithm

    Directory of Open Access Journals (Sweden)

    Kazuyoshi Z. Nanjo

    2010-11-01

    Full Text Available This study provides an overview of relative-intensity (RI-based earthquake forecast models that have been submitted for the 5-year and 10-year testing classes and the 3-month class of the Italian experiment within the Collaboratory for the Study of Earthquake Predictability (CSEP. The RI algorithm starts as a binary forecast system based on the working assumption that future large earthquakes are considered likely to occur at sites of higher seismic activity in the past. The measure of RI is the simply counting of the number of past earthquakes, which is known as the RI of seismicity. To improve the RI forecast performance, we first expand the RI algorithm to become part of a general class of smoothed seismicity models. We then convert the RI representation from a binary system into a testable CSEP model that forecasts the numbers of earthquakes for the predefined magnitudes. Our parameter tuning for the CSEP models is based on the past seismicity. The final submission is a set of two numerical data files that were created by tuned 5-year and 10-year models and an executable computer code of a tuned 3-month model, to examine which testing class is more meaningful in terms of the RI hypothesis. The main purpose of our participation is to better understand the importance (or lack of importance of RI of seismicity for earthquake forecastability.

  12. A Hidden Markov Model for avalanche forecasting on Chowkibal ...

    Indian Academy of Sciences (India)

    ... different states of the model and Avalanche Activity Index (AAI) of a day, derived from the model input variables, as an observation. Validation of the model with independent data of two winters (2008–2009, 2009–2010) gives 80% accuracy for both day-1 and day-2. Comparison of various forecasting quality measures and ...

  13. Validation of Model Forecasts of the Ambient Solar Wind

    Science.gov (United States)

    Macneice, P. J.; Hesse, M.; Kuznetsova, M. M.; Rastaetter, L.; Taktakishvili, A.

    2009-01-01

    Independent and automated validation is a vital step in the progression of models from the research community into operational forecasting use. In this paper we describe a program in development at the CCMC to provide just such a comprehensive validation for models of the ambient solar wind in the inner heliosphere. We have built upon previous efforts published in the community, sharpened their definitions, and completed a baseline study. We also provide first results from this program of the comparative performance of the MHD models available at the CCMC against that of the Wang-Sheeley-Arge (WSA) model. An important goal of this effort is to provide a consistent validation to all available models. Clearly exposing the relative strengths and weaknesses of the different models will enable forecasters to craft more reliable ensemble forecasting strategies. Models of the ambient solar wind are developing rapidly as a result of improvements in data supply, numerical techniques, and computing resources. It is anticipated that in the next five to ten years, the MHD based models will supplant semi-empirical potential based models such as the WSA model, as the best available forecast models. We anticipate that this validation effort will track this evolution and so assist policy makers in gauging the value of past and future investment in modeling support.

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

    OpenAIRE

    Haixiang Zang; Lei Fan; Mian Guo; Zhinong Wei; Guoqiang Sun; Li Zhang

    2016-01-01

    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 (EE...

  15. 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.

  16. 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.

  17. The ability of a multi-model seasonal forecasting ensemble to forecast the frequency of warm, cold and wet extremes

    Directory of Open Access Journals (Sweden)

    Acacia S. Pepler

    2015-09-01

    Full Text Available Dynamical models are now widely used to provide forecasts of above or below average seasonal mean temperatures and precipitation, with growing interest in their ability to forecast climate extremes on a seasonal time scale. This study assesses the skill of the ENSEMBLES multi-model ensemble to forecast the 90th and 10th percentiles of both seasonal temperature and precipitation, using a number of metrics of ‘extremeness’. Skill is generally similar or slightly lower to that for seasonal means, with skill strongly influenced by the El Niño-Southern Oscillation. As documented in previous studies, much of the skill in forecasting extremes can be related to skill in forecasting the seasonal mean value, with skill for extremes generally lower although still significant. Despite this, little relationship is found between the skill of forecasting the upper and lower tails of the distribution of daily values.

  18. Improving the Performance of Water Demand Forecasting Models by Using Weather Input

    NARCIS (Netherlands)

    Bakker, M.; Van Duist, H.; Van Schagen, K.; Vreeburg, J.; Rietveld, L.

    2014-01-01

    Literature shows that water demand forecasting models which use water demand as single input, are capable of generating a fairly accurate forecast. However, at changing weather conditions the forecasting errors are quite large. In this paper three different forecasting models are studied: an

  19. Development of infill drilling recovery models for carbonates reservoirs using neural networks and multivariate statistical as a novel method

    International Nuclear Information System (INIS)

    Soto, R; Wu, Ch. H; Bubela, A M

    1999-01-01

    This work introduces a novel methodology to improve reservoir characterization models. In this methodology we integrated multivariate statistical analyses, and neural network models for forecasting the infill drilling ultimate oil recovery from reservoirs in San Andres and Clearfork carbonate formations in west Texas. Development of the oil recovery forecast models help us to understand the relative importance of dominant reservoir characteristics and operational variables, reproduce recoveries for units included in the database, forecast recoveries for possible new units in similar geological setting, and make operational (infill drilling) decisions. The variety of applications demands the creation of multiple recovery forecast models. We have developed intelligent software (Soto, 1998), oilfield intelligence (01), as an engineering tool to improve the characterization of oil and gas reservoirs. 01 integrates neural networks and multivariate statistical analysis. It is composed of five main subsystems: data input, preprocessing, architecture design, graphic design, and inference engine modules. One of the challenges in this research was to identify the dominant and the optimum number of independent variables. The variables include porosity, permeability, water saturation, depth, area, net thickness, gross thickness, formation volume factor, pressure, viscosity, API gravity, number of wells in initial water flooding, number of wells for primary recovery, number of infill wells over the initial water flooding, PRUR, IWUR, and IDUR. Multivariate principal component analysis is used to identify the dominant and the optimum number of independent variables. We compared the results from neural network models with the non-parametric approach. The advantage of the non-parametric regression is that it is easy to use. The disadvantage is that it retains a large variance of forecast results for a particular data set. We also used neural network concepts to develop recovery

  20. Hydrological now- and forecasting : Integration of operationally available remotely sensed and forecasted hydrometeorological variables into distributed hydrological models

    NARCIS (Netherlands)

    Schuurmans, J.M.

    2008-01-01

    Keywords: hydrology, models, soil moisture, rainfall, radar, rain gauge, remote sensing, evapotranspiration, forecasting, numerical weather prediction, Netherlands, Langbroekerwetering, Lopikerwaard. Computer simulation models are an important tool for hydrologists. With these models they can

  1. 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

  2. Fuzzy Temporal Logic Based Railway Passenger Flow Forecast Model

    Science.gov (United States)

    Dou, Fei; Jia, Limin; Wang, Li; Xu, Jie; Huang, Yakun

    2014-01-01

    Passenger flow forecast is of essential importance to the organization of railway transportation and is one of the most important basics for the decision-making on transportation pattern and train operation planning. Passenger flow of high-speed railway features the quasi-periodic variations in a short time and complex nonlinear fluctuation because of existence of many influencing factors. In this study, a fuzzy temporal logic based passenger flow forecast model (FTLPFFM) is presented based on fuzzy logic relationship recognition techniques that predicts the short-term passenger flow for high-speed railway, and the forecast accuracy is also significantly improved. An applied case that uses the real-world data illustrates the precision and accuracy of FTLPFFM. For this applied case, the proposed model performs better than the k-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models. PMID:25431586

  3. Modeling and Forecasting Electricity Demand in Azerbaijan Using Cointegration Techniques

    Directory of Open Access Journals (Sweden)

    Fakhri J. Hasanov

    2016-12-01

    Full Text Available Policymakers in developing and transitional economies require sound models to: (i understand the drivers of rapidly growing energy consumption and (ii produce forecasts of future energy demand. This paper attempts to model electricity demand in Azerbaijan and provide future forecast scenarios—as far as we are aware this is the first such attempt for Azerbaijan using a comprehensive modelling framework. Electricity consumption increased and decreased considerably in Azerbaijan from 1995 to 2013 (the period used for the empirical analysis—it increased on average by about 4% per annum from 1995 to 2006 but decreased by about 4½% per annum from 2006 to 2010 and increased thereafter. It is therefore vital that Azerbaijani planners and policymakers understand what drives electricity demand and be able to forecast how it will grow in order to plan for future power production. However, modeling electricity demand for such a country has many challenges. Azerbaijan is rich in energy resources, consequently GDP is heavily influenced by oil prices; hence, real non-oil GDP is employed as the activity driver in this research (unlike almost all previous aggregate energy demand studies. Moreover, electricity prices are administered rather than market driven. Therefore, different cointegration and error correction techniques are employed to estimate a number of per capita electricity demand models for Azerbaijan, which are used to produce forecast scenarios for up to 2025. The resulting estimated models (in terms of coefficients, etc. and forecasts of electricity demand for Azerbaijan in 2025 prove to be very similar; with the Business as Usual forecast ranging from about of 19½ to 21 TWh.

  4. 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...

  5. Mountain range specific analog weather forecast model for ...

    Indian Academy of Sciences (India)

    Home; Journals; Journal of Earth System Science; Volume 117; Issue 5. Mountain range specific ... Mountain range specific analog weather forecast model is developed utilizing surface weather observations of reference stations in each mountain range in northwest Himalaya (NW-Himalaya).The model searches past ...

  6. Forecasting flood-prone areas using Shannon's entropy model

    Indian Academy of Sciences (India)

    Then, flood susceptibility forecasting map was provided and model accuracy evaluation was conducted using ROC curve and 30% flooding areas express good precision of the model (73.5%) for the study area. ... Department of Watershed Management Engineering, Faculty of Agriculture, Lorestan University, Lorestan, Iran.

  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. 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

  9. Alaska North Slope regional gas hydrate production modeling forecasts

    Science.gov (United States)

    Wilson, S.J.; Hunter, R.B.; Collett, T.S.; Hancock, S.; Boswell, R.; Anderson, B.J.

    2011-01-01

    A series of gas hydrate development scenarios were created to assess the range of outcomes predicted for the possible development of the "Eileen" gas hydrate accumulation, North Slope, Alaska. Production forecasts for the "reference case" were built using the 2002 Mallik production tests, mechanistic simulation, and geologic studies conducted by the US Geological Survey. Three additional scenarios were considered: A "downside-scenario" which fails to identify viable production, an "upside-scenario" describes results that are better than expected. To capture the full range of possible outcomes and balance the downside case, an "extreme upside scenario" assumes each well is exceptionally productive.Starting with a representative type-well simulation forecasts, field development timing is applied and the sum of individual well forecasts creating the field-wide production forecast. This technique is commonly used to schedule large-scale resource plays where drilling schedules are complex and production forecasts must account for many changing parameters. The complementary forecasts of rig count, capital investment, and cash flow can be used in a pre-appraisal assessment of potential commercial viability.Since no significant gas sales are currently possible on the North Slope of Alaska, typical parameters were used to create downside, reference, and upside case forecasts that predict from 0 to 71??BM3 (2.5??tcf) of gas may be produced in 20 years and nearly 283??BM3 (10??tcf) ultimate recovery after 100 years.Outlining a range of possible outcomes enables decision makers to visualize the pace and milestones that will be required to evaluate gas hydrate resource development in the Eileen accumulation. Critical values of peak production rate, time to meaningful production volumes, and investments required to rule out a downside case are provided. Upside cases identify potential if both depressurization and thermal stimulation yield positive results. An "extreme upside

  10. 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.

  11. Intercomparison of mesoscale meteorological models for precipitation forecasting

    Directory of Open Access Journals (Sweden)

    E. Richard

    2003-01-01

    Full Text Available In the framework of the RAPHAEL EU project, a series of past heavy precipitation events has been simulated with different meteorological models. Rainfall hindcasts and forecasts have been produced by four models in use at various meteorological services or research centres of Italy, Canada, France and Switzerland. The paper is focused on the comparison of the computed precipitation fields with the available surface observations. The comparison is carried out for three meteorological situations which lead to severe flashflood over the Toce-Ticino catchment in Italy (6599 km2 or the Ammer catchment (709 km2 in Germany. The results show that all four models reproduced the occurrence of these heavy precipitation events. The accuracy of the computed precipitation appears to be more case-dependent than model-dependent. The sensitivity of the computed rainfall to the boundary conditions (hindcast v. forecast was found to be rather weak, indicating that a flood forecasting system based upon a numerical meteo-hydrological simulation could be feasible in an operational context. Keywords: meteorological models, precipitation forecast

  12. 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.

  13. Two empirical models for short-term forecast of Kp

    Science.gov (United States)

    Luo, B.; Liu, S.; Gong, J.

    2017-03-01

    In this paper, two empirical models are developed for short-term forecast of the Kp index, taking advantage of solar wind-magnetosphere coupling functions proposed by the research community. Both models are based on the data for years 1995 to 2004. Model 1 mainly uses solar wind parameters as the inputs, while model 2 also utilizes the previous measured Kp value. Finally, model 1 predicts Kp with a linear correlation coefficient (r) of 0.91, a prediction efficiency (PE) of 0.81, and a root-mean-square (RMS) error of 0.59. Model 2 gives an r of 0.92, a PE of 0.84, and an RMS error of 0.57. The two models are validated through out-of-sample test for years 2005 to 2013, which also yields high forecast accuracy. Unlike in the other models reported in the literature, we are taking the response time of the magnetosphere to external solar wind at the Earth explicitly in the modeling. Statistically, the time delay in the models turns out to be about 30 min. By introducing this term, both the accuracy and lead time of the model forecast are improved. Through verification and validation, the models can be used in operational geomagnetic storm warnings with reliable performance.

  14. Temperature sensitivity of a numerical pollen forecast model

    Science.gov (United States)

    Scheifinger, Helfried; Meran, Ingrid; Szabo, Barbara; Gallaun, Heinz; Natali, Stefano; Mantovani, Simone

    2016-04-01

    Allergic rhinitis has become a global health problem especially affecting children and adolescence. Timely and reliable warning before an increase of the atmospheric pollen concentration means a substantial support for physicians and allergy suffers. Recently developed numerical pollen forecast models have become means to support the pollen forecast service, which however still require refinement. One of the problem areas concerns the correct timing of the beginning and end of the flowering period of the species under consideration, which is identical with the period of possible pollen emission. Both are governed essentially by the temperature accumulated before the entry of flowering and during flowering. Phenological models are sensitive to a bias of the temperature. A mean bias of -1°C of the input temperature can shift the entry date of a phenological phase for about a week into the future. A bias of such an order of magnitude is still possible in case of numerical weather forecast models. If the assimilation of additional temperature information (e.g. ground measurements as well as satellite-retrieved air / surface temperature fields) is able to reduce such systematic temperature deviations, the precision of the timing of phenological entry dates might be enhanced. With a number of sensitivity experiments the effect of a possible temperature bias on the modelled phenology and the pollen concentration in the atmosphere is determined. The actual bias of the ECMWF IFS 2 m temperature will also be calculated and its effect on the numerical pollen forecast procedure presented.

  15. Short-term integrated forecasting system : 1993 model documentation report

    Science.gov (United States)

    1993-12-01

    The purpose of this report is to define the Short-Term Integrated Forecasting System (STIFS) and describe its basic properties. The Energy Information Administration (EIA) of the U.S. Energy Department (DOE) developed the STIFS model to generate shor...

  16. 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

  17. A Model for Understanding Management Manpower: Forecasting and Planning

    Science.gov (United States)

    Deckard, Noble S.; Lessey, Kenneth W.

    1975-01-01

    The authors, realizing the importance of continuous organizational reappraisal of manpower needs and strengths, have developed a model based on supply of management manpower and demand for management manpower. Without a manpower forecasting/planning program, the future needs of the organization are reduced to guesswork. (EA)

  18. 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

  19. 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

  20. Interval forecasts of a novelty hybrid model for wind speeds

    Directory of Open Access Journals (Sweden)

    Shanshan Qin

    2015-11-01

    Full Text Available The utilization of wind energy, as a booming technology in the field of renewable energies, has been highly regarded around the world. Quantification of uncertainties associated with accurate wind speed forecasts is essential for regulating wind power generation and integration. However, it remains difficult work primarily due to the stochastic and nonlinear characteristics of wind speed series. Traditional models for wind speed forecasting mostly focus on generating certain predictive values, which cannot properly handle uncertainties. For quantifying potential uncertainties, a hybrid model constructed by the Cuckoo Search Optimization (CSO-based Back Propagation Neural Network (BPNN is proposed to establish wind speed interval forecasts (IFs by estimating the lower and upper bounds. The quality of IFs is assessed quantitatively using IFs coverage probability (IFCP and IFs normalized average width (IFNAW. Moreover, to assess the overall quality of IFs comprehensively, a tradeoff between informativeness (IFNAW and validity (IFCP of IFs is examined by coverage width-based criteria (CWC. As an applicative study, wind speeds from the Xinjiang Region in China are used to validate the proposed hybrid model. The results demonstrate that the proposed model can construct higher quality IFs for short-term wind speed forecasts.

  1. Forecasting Labour Productivity Growth in Norway for the Period 2012-2021 Using ARIMA Models

    Directory of Open Access Journals (Sweden)

    Pirooz Samavati

    2013-06-01

    Full Text Available There appeared to be a change in labour productivity growth in Norway (a fall in the growth rate in the middle of the 2000s, followed by a slight recovery at the end of the period under consideration (1971-2011. The 2007-2009 financial and economic crisis in Norway (which resulted from the banking crisis caused an even greater drop in labour productivity growth to the extent that it in 2008 it reached its lowest point over the last three decades. After 2008, labour productivity growth started to increase. In this paper, in order to forecast time-series labour productivity growth in Norway for the period 2012-2021, the ARIMA model is fitted to Norwegian time-series labour productivity growth data obtained in the period 1971-2011. Using the Box-Jenkins model selection methodology, ARIMA (1, 1, 1 with no constant is selected as an appropriate model. As the selected ARIMA model indicates, labour productivity growth in Norway shall continue to increase very slowly and will ultimately reach a non-zero constant in the forecast period (2012-2021 following its recovery after 2008. Long-term forecasts for time-series labour productivity growth in Norway using ARIMA (1, 1, 1 with no constant will also reach a non-zero constant. Initially, it might be concluded that slow technological development as a result of the 2007-2009 financial and economic crisis could explain the slowdown in the recovery of labour productivity growth both in the forecast period (2012-2021 and over the longer term. However, due to the fact that the 2007-2009 financial and economic crisis has changed the underlying process which Norwegian labour productivity growth rate followed in the immediately preceding period, and that a technological revolution, which can be considered as a contributing factor, also took place in that period, it seems unlikely that a single labour productivity growth time series will be rich enough to describe the variation in the data. From the data and the

  2. 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)

  3. 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)

  4. 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...

  5. Improving Global Forecast System of extreme precipitation events with regional statistical model: Application of quantile-based probabilistic forecasts

    Science.gov (United States)

    Shastri, Hiteshri; Ghosh, Subimal; Karmakar, Subhankar

    2017-02-01

    Forecasting of extreme precipitation events at a regional scale is of high importance due to their severe impacts on society. The impacts are stronger in urban regions due to high flood potential as well high population density leading to high vulnerability. Although significant scientific improvements took place in the global models for weather forecasting, they are still not adequate at a regional scale (e.g., for an urban region) with high false alarms and low detection. There has been a need to improve the weather forecast skill at a local scale with probabilistic outcome. Here we develop a methodology with quantile regression, where the reliably simulated variables from Global Forecast System are used as predictors and different quantiles of rainfall are generated corresponding to that set of predictors. We apply this method to a flood-prone coastal city of India, Mumbai, which has experienced severe floods in recent years. We find significant improvements in the forecast with high detection and skill scores. We apply the methodology to 10 ensemble members of Global Ensemble Forecast System and find a reduction in ensemble uncertainty of precipitation across realizations with respect to that of original precipitation forecasts. We validate our model for the monsoon season of 2006 and 2007, which are independent of the training/calibration data set used in the study. We find promising results and emphasize to implement such data-driven methods for a better probabilistic forecast at an urban scale primarily for an early flood warning.

  6. 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

  7. Using phenomenological models for forecasting the 2015 Ebola challenge

    Directory of Open Access Journals (Sweden)

    Bruce Pell

    2018-03-01

    Full Text Available Background: The rising number of novel pathogens threatening the human population has motivated the application of mathematical modeling for forecasting the trajectory and size of epidemics. Materials and methods: We summarize the real-time forecasting results of the logistic equation during the 2015 Ebola challenge focused on predicting synthetic data derived from a detailed individual-based model of Ebola transmission dynamics and control. We also carry out a post-challenge comparison of two simple phenomenological models. In particular, we systematically compare the logistic growth model and a recently introduced generalized Richards model (GRM that captures a range of early epidemic growth profiles ranging from sub-exponential to exponential growth. Specifically, we assess the performance of each model for estimating the reproduction number, generate short-term forecasts of the epidemic trajectory, and predict the final epidemic size. Results: During the challenge the logistic equation consistently underestimated the final epidemic size, peak timing and the number of cases at peak timing with an average mean absolute percentage error (MAPE of 0.49, 0.36 and 0.40, respectively. Post-challenge, the GRM which has the flexibility to reproduce a range of epidemic growth profiles ranging from early sub-exponential to exponential growth dynamics outperformed the logistic growth model in ascertaining the final epidemic size as more incidence data was made available, while the logistic model underestimated the final epidemic even with an increasing amount of data of the evolving epidemic. Incidence forecasts provided by the generalized Richards model performed better across all scenarios and time points than the logistic growth model with mean RMS decreasing from 78.00 (logistic to 60.80 (GRM. Both models provided reasonable predictions of the effective reproduction number, but the GRM slightly outperformed the logistic growth model with a MAPE of 0

  8. 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.

  9. A Novel Fuzzy Document Based Information Retrieval Model for Forecasting

    Directory of Open Access Journals (Sweden)

    Partha Roy

    2017-06-01

    Full Text Available Information retrieval systems are generally used to find documents that are most appropriate according to some query that comes dynamically from users. In this paper a novel Fuzzy Document based Information Retrieval Model (FDIRM is proposed for the purpose of Stock Market Index forecasting. The novelty of proposed approach is a modified tf-idf scoring scheme to predict the future trend of the stock market index. The contribution of this paper has two dimensions, 1 In the proposed system the simple time series is converted to an enriched fuzzy linguistic time series with a unique approach of incorporating market sentiment related information along with the price and 2 A unique approach is followed while modeling the information retrieval (IR system which converts a simple IR system into a forecasting system. From the performance comparison of FDIRM with standard benchmark models it can be affirmed that the proposed model has a potential of becoming a good forecasting model. The stock market data provided by Standard & Poor’s CRISIL NSE Index 50 (CNX NIFTY-50 index of National Stock Exchange of India (NSE is used to experiment and validate the proposed model. The authentic data for validation and experimentation is obtained from http://www.nseindia.com which is the official website of NSE. A java program is under construction to implement the model in real-time with graphical users’ interface.

  10. 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.)

  11. The use of HBV model for flash flood forecasting

    Directory of Open Access Journals (Sweden)

    M. Kobold

    2006-01-01

    Full Text Available The standard conceptual HBV model was originally developed with daily data and is normally operated on daily time step. But many floods in Slovenia are usually flash floods as result of intense frontal precipitation combined with orographic enhancement. Peak discharges are maintained only for hours or even minutes. To use the HBV model for flash flood forecasting, the version of HBV-96 has been applied on the catchment with complex topography with the time step of one hour. The recording raingauges giving hourly values of precipitation have been taken in calibration of the model. The uncertainty of simulated runoff is mainly the result of precipitation uncertainty associated with the mean areal precipitation and is higher for mountainous catchments. Therefore the influence of number of raingauges used to derive the areal precipitation by the method of Thiessen polygons was investigated. The quantification of hydrological uncertainty has been performed by analysis of sensitivity of the HBV model to error in precipitation input. The results show that an error of 10% in the amount of precipitation causes an error of 17% in the peak of flood wave. The polynomial equations showing the relationship between the errors in rainfall amounts and peak discharges were derived for two water stations on the Savinja catchment. Simulated discharges of half-yearly runs demonstrate the applicability of the HBV model for flash flood forecasting using the mesoscale meteorological forecasts of ALADIN/SI model as input precipitation data.

  12. 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...

  13. 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

  14. 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

  15. Using the Advanced Research Version of the Weather Research and Forecasting Model (WRF-ARW) to Forecast Turbulence at Small Scales

    National Research Council Canada - National Science Library

    Passner, Jeffrey E

    2008-01-01

    ...) as well as for longer-range forecasting support. The model utilized to investigate fine-scale weather processes, the Advanced Research version of the Weather Research and Forecasting model (WRF-ARW...

  16. 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...

  17. 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.

  18. 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.

  19. Forecasting Macroeconomic Variables using Neural Network Models and Three Automated Model Selection Techniques

    DEFF Research Database (Denmark)

    Kock, Anders Bredahl; Teräsvirta, Timo

    In this paper we consider the forecasting performance of a well-defined class of flexible models, the so-called single hidden-layer feedforward neural network models. A major aim of our study is to find out whether they, due to their flexibility, are as useful tools in economic forecasting as some...... previous studies have indicated. 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. In fact, their parameters are not even globally...... on the linearisation idea: the Marginal Bridge Estimator and Autometrics. Second, one must decide whether forecasting should be carried out recursively or directly. Comparisons of these two methodss exist for linear models and here these comparisons are extended to neural networks. Finally, a nonlinear model...

  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. Reserve growth in oil pools of Alberta: Model and forecast

    Science.gov (United States)

    Verma, M.; Cook, T.

    2010-01-01

    Reserve growth is recognized as a major component of additions to reserves in most oil provinces around the world, particularly in mature provinces. It takes place as a result of the discovery of new pools/reservoirs and extensions of known pools within existing fields, improved knowledge of reservoirs over time leading to a change in estimates of original oil-in-place, and improvement in recovery factor through the application of new technology, such as enhanced oil recovery methods, horizontal/multilateral drilling, and 4D seismic. A reserve growth study was conducted on oil pools in Alberta, Canada, with the following objectives: 1) evaluate historical oil reserve data in order to assess the potential for future reserve growth; 2) develop reserve growth models/ functions to help forecast hydrocarbon volumes; 3) study reserve growth sensitivity to various parameters (for example, pool size, porosity, and oil gravity); and 4) compare reserve growth in oil pools and fields in Alberta with those from other large petroleum provinces around the world. The reported known recoverable oil exclusive of Athabasca oil sands in Alberta increased from 4.5 billion barrels of oil (BBO) in 1960 to 17 BBO in 2005. Some of the pools that were included in the existing database were excluded from the present study for lack of adequate data. Therefore, the known recoverable oil increased from 4.2 to 13.9 BBO over the period from 1960 through 2005, with new discoveries contributing 3.7 BBO and reserve growth adding 6 BBO. This reserve growth took place mostly in pools with more than 125,000 barrels of known recoverable oil. Pools with light oil accounted for most of the total known oil volume, therefore reflecting the overall pool growth. Smaller pools, in contrast, shrank in their total recoverable volumes over the years. Pools with heavy oil (gravity less than 20o API) make up only a small share (3.8 percent) of the total recoverable oil; they showed a 23-fold growth compared to

  2. Uncertainty calculation in transport models and forecasts

    DEFF Research Database (Denmark)

    Manzo, Stefano; Prato, Carlo Giacomo

    . Forthcoming: European Journal of Transport and Infrastructure Research, 15-3, 64-72. 4 The last paper4 examined uncertainty in the spatial composition of residence and workplace locations in the Danish National Transport Model. Despite the evidence that spatial structure influences travel behaviour...... to increase the quality of the decision process and to develop robust or adaptive plans. In fact, project evaluation processes that do not take into account model uncertainty produce not fully informative and potentially misleading results so increasing the risk inherent to the decision to be taken...

  3. 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...

  4. 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)

  5. 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

  6. 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.

  7. A short-range objective nocturnal temperature forecasting model

    Science.gov (United States)

    Sutherland, R. A.

    1980-01-01

    A relatively simple, objective, nocturnal temperature forecasting model suitable for freezing and near-freezing conditions has been designed so that a user, presumably a weather forecaster, can put in standard meteorological data at a particular location and receive an hour-by-hour prediction of surface and air temperatures for that location for an entire night. The user has the option of putting in his own estimates of wind speeds and background sky radiation which are treated as independent variables. An analysis of 141 test runs show that 57.4% of the time the model predicts to within 1 C for the best cases and to within 3 C for 98.0% of all cases.

  8. 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.

  9. 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 Institutional support: RVO:67985556 Keywords : Long memory * multifractal models * price durations * realized volatility * whittle estimation Subject RIV: AH - Economics OBOR OECD: Finance Impact factor: 1.333, year: 2016 http://library.utia.cas.cz/separaty/2017/E/barunik-0478483.pdf

  10. 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

  11. A Bayesian Technique for Selecting a Linear Forecasting Model

    OpenAIRE

    Ramona L. Trader

    1983-01-01

    The specification of a forecasting model is considered in the context of linear multiple regression. Several potential predictor variables are available, but some of them convey little information about the dependent variable which is to be predicted. A technique for selecting the "best" set of predictors which takes into account the inherent uncertainty in prediction is detailed. In addition to current data, there is often substantial expert opinion available which is relevant to the forecas...

  12. 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,...

  13. 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....

  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. 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.

  16. 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

    In most operational wind power forecasting systems statistical methods are applied to map wind forecasts from numerical weather prediction (NWP) models into wind power forecasts. NWP models are complex mathematical models of the atmosphere that divide the earth’s surface into a grid. The spatial...... 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...

  17. 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.

  18. 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.

  19. 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)

  20. 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

  1. Optimizing Computing Platforms for Climate-Driven Ecological Forecasting Models

    Science.gov (United States)

    Farley, S. S.; Williams, J. W.

    2016-12-01

    Species distribution models are widely used, climate-driven ecological forecasting tools that use machine-learning techniques to predict species range shifts and ecological responses to 21st century climate change. As high-resolution modern and fossil biodiversity data becomes increasingly available and statistical learning methods become more computationally intensive, choosing the correct computing configuration on which to run these models becomes more important. With a variety of low-cost cloud and desktop computing options available, users of forecasting models must balance performance gains achieved by provisioning more powerful hardware with the cost of using these resources. We present a framework for estimating the optimal computing solution for a given modeling activity. We argue that this framework is capable of identifying the optimal computing solution - the one that maximizes model accuracy while minimizing resource cost and computing time. Our framework is built on constituent models of algorithm execution time, predictive skill, and computing cost. We demonstrate the results of the framework using four leading species distribution models: multivariate adaptive regression splines, generalized additive models, support vector machines, and boosted regression trees. The constituent models themselves are shown to have high predictive accuracy, and can be used independently to estimate the effects of using larger input datasets, such as those that incorporate data from the fossil record. When used together, our framework shows highly significant predictive ability, and is designed to be used by researchers to inform future computing provisioning strategies.

  2. Forecasting Demand Using Survival Modeling: an application to US prisons

    Directory of Open Access Journals (Sweden)

    Joanna Baker

    1994-11-01

    Full Text Available A systems approach to modeling demand which incorporates survival modeling is applied to the problem of prison population projection. The approach models the flow of inmates through the prison system and differs from earlier approaches by exploiting the differences in the incarceration hazard rates of individuals in the general population and those who have previously been incarcerated and explicitly considering the impact of constrained prison capacity on release policy and future admissions. The methodology capitalizes on the impact of recursion in the prison population and reduces the amount and complexity of data required for long-term forecasts.. First-time arrivals to prison are modeled as a Poisson process arising from the general population; recidivist arrivals are modeled using a failure model, where the reincarceration hazard rate is a function of age and race. The model is demonstrated for the state of North Carolina located in the Southeastern region of the United States. The effect of limited prison capacity on the mean of the time-served distribution is shown. The results demonstrate that an early release policy will generate an increase in prison admissions through the return to prison of former inmates. Further, the results show that a systems approach to modeling of prison demand which includes the non-linear effect of recidivism, i.e., survival modeling, has a significant impact on the accuracy of forecasts.

  3. Forecasting Austrian national elections: The Grand Coalition model

    Science.gov (United States)

    Aichholzer, Julian; Willmann, Johanna

    2014-01-01

    Forecasting the outcomes of national elections has become established practice in several democracies. In the present paper, we develop an economic voting model for forecasting the future success of the Austrian ‘grand coalition’, i.e., the joint electoral success of the two mainstream parties SPOE and OEVP, at the 2013 Austrian Parliamentary Elections. Our main argument is that the success of both parties is strongly tied to the accomplishments of the Austrian system of corporatism, that is, the Social Partnership (Sozialpartnerschaft), in providing economic prosperity. Using data from Austrian national elections between 1953 and 2008 (n=18), we rely on the following predictors in our forecasting model: (1) unemployment rates, (2) previous incumbency of the two parties, and (3) dealignment over time. We conclude that, in general, the two mainstream parties benefit considerably from low unemployment rates, and are weakened whenever they have previously formed a coalition government. Further, we show that they have gradually been losing a good share of their voter basis over recent decades. PMID:26339109

  4. 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.

  5. Sol-Terra - AN Operational Space Weather Forecasting Model Framework

    Science.gov (United States)

    Bisi, M. M.; Lawrence, G.; Pidgeon, A.; Reid, S.; Hapgood, M. A.; Bogdanova, Y.; Byrne, J.; Marsh, M. S.; Jackson, D.; Gibbs, M.

    2015-12-01

    The SOL-TERRA project is a collaboration between RHEA Tech, the Met Office, and RAL Space funded by the UK Space Agency. The goal of the SOL-TERRA project is to produce a Roadmap for a future coupled Sun-to-Earth operational space weather forecasting system covering domains from the Sun down to the magnetosphere-ionosphere-thermosphere and neutral atmosphere. The first stage of SOL-TERRA is underway and involves reviewing current models that could potentially contribute to such a system. Within a given domain, the various space weather models will be assessed how they could contribute to such a coupled system. This will be done both by reviewing peer reviewed papers, and via direct input from the model developers to provide further insight. Once the models have been reviewed then the optimal set of models for use in support of forecast-based SWE modelling will be selected, and a Roadmap for the implementation of an operational forecast-based SWE modelling framework will be prepared. The Roadmap will address the current modelling capability, knowledge gaps and further work required, and also the implementation and maintenance of the overall architecture and environment that the models will operate within. The SOL-TERRA project will engage with external stakeholders in order to ensure independently that the project remains on track to meet its original objectives. A group of key external stakeholders have been invited to provide their domain-specific expertise in reviewing the SOL-TERRA project at critical stages of Roadmap preparation; namely at the Mid-Term Review, and prior to submission of the Final Report. This stakeholder input will ensure that the SOL-TERRA Roadmap will be enhanced directly through the input of modellers and end-users. The overall goal of the SOL-TERRA project is to develop a Roadmap for an operational forecast-based SWE modelling framework with can be implemented within a larger subsequent activity. The SOL-TERRA project is supported within

  6. Accurate diode behavioral model with reverse recovery

    Science.gov (United States)

    Banáš, Stanislav; Divín, Jan; Dobeš, Josef; Paňko, Václav

    2018-01-01

    This paper deals with the comprehensive behavioral model of p-n junction diode containing reverse recovery effect, applicable to all standard SPICE simulators supporting Verilog-A language. The model has been successfully used in several production designs, which require its full complexity, robustness and set of tuning parameters comparable with standard compact SPICE diode model. The model is like standard compact model scalable with area and temperature and can be used as a stand-alone diode or as a part of more complex device macro-model, e.g. LDMOS, JFET, bipolar transistor. The paper briefly presents the state of the art followed by the chapter describing the model development and achieved solutions. During precise model verification some of them were found non-robust or poorly converging and replaced by more robust solutions, demonstrated in the paper. The measurement results of different technologies and different devices compared with a simulation using the new behavioral model are presented as the model validation. The comparison of model validation in time and frequency domains demonstrates that the implemented reverse recovery effect with correctly extracted parameters improves the model simulation results not only in switching from ON to OFF state, which is often published, but also its impedance/admittance frequency dependency in GHz range. Finally the model parameter extraction and the comparison with SPICE compact models containing reverse recovery effect is presented.

  7. 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

  8. 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.

  9. 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.

  10. Forecasting the yield curve - Forecast performance of the dynamic Nelson-Siegel model from 1971 to 2008

    OpenAIRE

    Molenaars, Tomas K.; Reinerink, Nick H.; Hemminga, Marcus A.

    2013-01-01

    We define a parameter representing the relative forecast performance to compare forecasting results of different methods. By using this parameter, we analyze the performance of the dynamic Nelson-Siegel model and, for comparison, the first order autoregressive (AR(1)) model applied to a set of US bond yield data that covers a time span from November 1971 to December 2008. As a reference, we take the random walk model applied to the yield data. Our findings indicate that none of the models can...

  11. 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

  12. Application of artificial intelligence models in water quality forecasting.

    Science.gov (United States)

    Yeon, I S; Kim, J H; Jun, K W

    2008-06-01

    The real-time data of the continuous water quality monitoring station at the Pyeongchang river was analyzed separately during the rainy period and non-rainy period. Total organic carbon data observed during the rainy period showed a greater mean value, maximum value and standard deviation than the data observed during the non-rainy period. Dissolved oxygen values during the rainy period were lower than those observed during the non-rainy period. It was analyzed that the discharge due to rain fall from the basin affects the change of the water quality. A model for the forecasting of water quality was constructed and applied using the neural network model and the adaptive neuro-fuzzy inference system. Regarding the models of levenberg-marquardt neural network, modular neural network and adaptive neuro-fuzzy inference system, all three models showed good results for the simulation of total organic carbon. The levenberg-marquardt neural network and modular neural network models showed better results than the adaptive neuro-fuzzy inference system model in the forecasting of dissolved oxygen. The modular neural network model, which was applied with the qualitative data of time in addition to quantitative data, showed the least error.

  13. 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.

  14. 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)

  15. 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)

  16. 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)

  17. Learning Adaptive Forecasting Models from Irregularly Sampled Multivariate Clinical Data.

    Science.gov (United States)

    Liu, Zitao; Hauskrecht, Milos

    2016-02-01

    Building accurate predictive models of clinical multivariate time series is crucial for understanding of the patient condition, the dynamics of a disease, and clinical decision making. A challenging aspect of this process is that the model should be flexible and adaptive to reflect well patient-specific temporal behaviors and this also in the case when the available patient-specific data are sparse and short span. To address this problem we propose and develop an adaptive two-stage forecasting approach for modeling multivariate, irregularly sampled clinical time series of varying lengths. The proposed model (1) learns the population trend from a collection of time series for past patients; (2) captures individual-specific short-term multivariate variability; and (3) adapts by automatically adjusting its predictions based on new observations. The proposed forecasting model is evaluated on a real-world clinical time series dataset. The results demonstrate the benefits of our approach on the prediction tasks for multivariate, irregularly sampled clinical time series, and show that it can outperform both the population based and patient-specific time series prediction models in terms of prediction accuracy.

  18. 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

  19. 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...

  20. Forecasting energy consumption using a grey model improved by incorporating genetic programming

    International Nuclear Information System (INIS)

    Lee, Yi-Shian; Tong, Lee-Ing

    2011-01-01

    Energy consumption is an important economic index, which reflects the industrial development of a city or a country. Forecasting energy consumption by conventional statistical methods usually requires the making of assumptions such as the normal distribution of energy consumption data or on a large sample size. However, the data collected on energy consumption are often very few or non-normal. Since a grey forecasting model, based on grey theory, can be constructed for at least four data points or ambiguity data, it can be adopted to forecast energy consumption. In some cases, however, a grey forecasting model may yield large forecasting errors. To minimize such errors, this study develops an improved grey forecasting model, which combines residual modification with genetic programming sign estimation. Finally, a real case of Chinese energy consumption is considered to demonstrate the effectiveness of the proposed forecasting model.

  1. Spatio-temporal modelling for short term wind power forecasts. Why, when and how.

    Science.gov (United States)

    Lenzi, Amanda; Steinsland, Ingelin; Pinson, Pierre

    2017-04-01

    This study is based on a case study of 349 wind farms in Western Denmark with available energy production every 15 minutes for 6 years. Our aim is to do short term forecasting up to 5 hours ahead based on previous observations. We want sharp and calibrated probabilistic forecasts for both individual wind farms and for aggregated energy production, for example the energy production in the whole region. To obtain this we propose two Bayesian spatio-temporal models, and obtain full probabilistic forecasts of wind power. The models are based on the stochastic partial differential equation (SPDE) approach to spatial-temporal modelling which enables fast inference using integrated nested Laplace approximations (INLA) as well as dimension reduction. We provide detailed analysis on the forecast performances on the individual and aggregated level based on appropriate metrics tailored for probability forecasts for both the spatial temporal models as well as for temporal models for individual wind farms. The case study as well as simulation studies demonstrate that forecasts that are individually reliable do not need to produce an aggregated forecasts that are reliable. Indeed, the case study shows that even when all individual forecasts are calibrated can the aggregated forecasts be so uncalibrated that less that 20% of the observations fall within the 95% forecast interval. T he results and methodology are both relevant for wind power forecasts in other regions as well as for spatial-temporal modeling and decisions in general.

  2. 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.

  3. 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)

  4. Research on Short-Term Wind Power Prediction Based on Combined Forecasting Models

    Directory of Open Access Journals (Sweden)

    Zhang Chi

    2016-01-01

    Full Text Available Short-Term wind power forecasting is crucial for power grid since the generated energy of wind farm fluctuates frequently. In this paper, a physical forecasting model based on NWP and a statistical forecasting model with optimized initial value in the method of BP neural network are presented. In order to make full use of the advantages of the models presented and overcome the limitation of the disadvantage, the equal weight model and the minimum variance model are established for wind power prediction. Simulation results show that the combination forecasting model is more precise than single forecasting model and the minimum variance combination model can dynamically adjust weight of each single method, restraining the forecasting error further.

  5. Lightning Forecasts and Data Assimilation into Numerical Weather Prediction Models

    Science.gov (United States)

    MacGorman, D. R.; Mansell, E. R.; Fierro, A.; Ziegler, C.

    2012-12-01

    This presentation reviews two aspects of lightning in numerical weather prediction (NWP) models: forecasting lightning and assimilating lightning data into NWP models to improve weather forecasts. One of the earliest routine forecasts of lightning was developed for fire weather operations. This approach used a multi-parameter regression analysis of archived cloud-to-ground (CG) lightning data and archived NWP data to optimize the combination of model state variables to use in forecast equations for various CG rates. Since then, understanding of how storms produce lightning has improved greatly. As the treatment of ice in microphysics packages used by NWP models has improved and the horizontal resolution of models has begun approaching convection-permitting scales (with convection-resolving scales on the horizon), it is becoming possible to use this improved understanding in NWP models to predict lightning more directly. An important role for data assimilation in NWP models is to depict the location, timing, and spatial extent of thunderstorms during model spin-up so that the effects of prior convection that can strongly influence future thunderstorm activity, such as updrafts and outflow boundaries, can be included in the initial state of a NWP model run. Radar data have traditionally been used, but systems that map lightning activity with varying degrees of coverage, detail, and detection efficiency are now available routinely over large regions and reveal information about storms that is complementary to the information provided by radar. Because data from lightning mapping systems are compact, easily handled, and reliably indicate the location and timing of thunderstorms, even in regions with little or no radar coverage, several groups have investigated techniques for assimilating these data into NWP models. This application will become even more valuable with the launch of the Geostationary Lightning Mapper on the GOES-R satellite, which will extend routine

  6. Space Weather Forecasts Driven by the ADAPT Model

    Science.gov (United States)

    Henney, C. J.; Arge, C. N.; Shurkin, K.; Schooley, A. K.; Hock, R. A.; White, S.

    2015-12-01

    In this presentation, we highlight recent progress to forecast key space weather parameters with the ADAPT (Air Force Data Assimilative Photospheric flux Transport) model. Driven by a magnetic flux transport model, ADAPT evolves global solar magnetic maps forward 1 to 7 days in the future to provide realistic estimates of the solar near-side field distribution used to forecast the solar wind, F10.7 (i.e., the solar 10.7 cm radio flux), extreme ultraviolet (EUV) and far ultraviolet (FUV) irradiance. Input to the ADAPT model includes solar near-side estimates of the inferred photospheric magnetic field from space-based (i.e., HMI) and ground-based (e.g., GONG & VSM) instruments. We summarize the recent findings that: 1) the sum of the absolute value of strong magnetic fields, associated with sunspots, is shown to correlate well with the observed daily F10.7 variability (Henney et al. 2012); and 2) the sum of the absolute value of weak magnetic fields, associated with plage regions, is shown to correlate well with EUV and FUV irradiance variability (Henney et al. 2015). In addition, recent progress to utilize the ADAPT global maps as input to the Wang-Sheeley-Arge (WSA) coronal and solar wind model is presented. We also discuss the challenges of observing less than half of the solar surface at any given time and the need for future magnetograph instruments near L1 and L5.

  7. Building a House Prices Forecasting Model in Hong Kong

    Directory of Open Access Journals (Sweden)

    Xin Janet

    2012-11-01

    Full Text Available This paper builds a house prices forecasting model for private residential houses in HongKong, based on general macroeconomic indicators, housing related data and demographicfactors for the period of 1980 to 2001. A reduce form economic model has been derivedfrom a multiple regression analysis where three sets and eight models were derived foranalysis and comparison. It is found that household income, land supply, population andmovements in the Hang Seng Index play an important role in explaining house pricemovements in Hong Kong. In addition, political events, as identified, cannot be ignored.However, the results of the models are unstable. It is suggested that the OLS may nota best method for house prices model in Hong Kong situation. Alternative methods aresuggested.

  8. Forecast model applied to quality control with autocorrelational data

    Directory of Open Access Journals (Sweden)

    Adriano Mendonça Souza

    2013-11-01

    Full Text Available This research approaches the prediction models applied to industrial processes, in order to check the stability of the process by means of control charts, applied to residues from linear modeling. The data used for analysis refers to the moisture content, permeability and compression resistance to the green (RCV, belonging to the casting process of green sand molding in A Company, which operates in the casting and machining, for which dynamic multivariate regression model was set. As the observations were auto-correlated, it was necessary to seek a mathematical model that produces independent and identically distribuibed residues. The models found make possible to understand the variables behavior, assisting in the achievement of the forecasts and in the monitoring of the referred process. Thus, it can be stated that the moisture content is very unstable comparing to the others variables.

  9. 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...

  10. COP21 climate negotiators' responses to climate model forecasts

    Science.gov (United States)

    Bosetti, Valentina; Weber, Elke; Berger, Loïc; Budescu, David V.; Liu, Ning; Tavoni, Massimo

    2017-02-01

    Policymakers involved in climate change negotiations are key users of climate science. It is therefore vital to understand how to communicate scientific information most effectively to this group. We tested how a unique sample of policymakers and negotiators at the Paris COP21 conference update their beliefs on year 2100 global mean temperature increases in response to a statistical summary of climate models' forecasts. We randomized the way information was provided across participants using three different formats similar to those used in Intergovernmental Panel on Climate Change reports. In spite of having received all available relevant scientific information, policymakers adopted such information very conservatively, assigning it less weight than their own prior beliefs. However, providing individual model estimates in addition to the statistical range was more effective in mitigating such inertia. The experiment was repeated with a population of European MBA students who, despite starting from similar priors, reported conditional probabilities closer to the provided models' forecasts than policymakers. There was also no effect of presentation format in the MBA sample. These results highlight the importance of testing visualization tools directly on the population of interest.

  11. 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

  12. 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.

  13. 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.

  14. 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

  15. Dynamic Hybrid Model for Short-Term Electricity Price Forecasting

    OpenAIRE

    Marin Cerjan; Marin Matijaš; Marko Delimar

    2014-01-01

    Accurate forecasting tools are essential in the operation of electric power systems, especially in deregulated electricity markets. Electricity price forecasting is necessary for all market participants to optimize their portfolios. In this paper we propose a hybrid method approach for short-term hourly electricity price forecasting. The paper combines statistical techniques for pre-processing of data and a multi-layer (MLP) neural network for forecasting electricity price and price spike det...

  16. 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

  17. Nonlinear time series modeling and forecasting the seismic data of the Hindu Kush region

    Science.gov (United States)

    Khan, Muhammad Yousaf; Mittnik, Stefan

    2017-11-01

    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.

  18. 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.

  19. 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.

  20. 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

  1. Multiobjective Optimization for the Forecasting Models on the Base of the Strictly Binary Trees

    OpenAIRE

    Nadezhda Astakhova; Liliya Demidova; Evgeny Nikulchev

    2016-01-01

    The optimization problem dealing with the development of the forecasting models on the base of strictly binary trees has been considered. The aim of paper is the comparative analysis of two optimization variants which are applied for the development of the forecasting models. Herewith the first optimization variant assumes the application of one quality indicator of the forecasting model named as the affinity indicator and the second variant realizes the application of two quality indicators ...

  2. A Capacity Forecast Model for Volatile Data in Maintenance Logistics

    Science.gov (United States)

    Berkholz, Daniel

    2009-05-01

    Maintenance, repair and overhaul processes (MRO processes) are elaborate and complex. Rising demands on these after sales services require reliable production planning and control methods particularly for maintaining valuable capital goods. Downtimes lead to high costs and an inability to meet delivery due dates results in severe contract penalties. Predicting the required capacities for maintenance orders in advance is often difficult due to unknown part conditions unless the goods are actually inspected. This planning uncertainty results in extensive capital tie-up by rising stock levels within the whole MRO network. The article outlines an approach to planning capacities when maintenance data forecasting is volatile. It focuses on the development of prerequisites for a reliable capacity planning model. This enables a quick response to maintenance orders by employing appropriate measures. The information gained through the model is then systematically applied to forecast both personnel capacities and the demand for spare parts. The improved planning reliability can support MRO service providers in shortening delivery times and reducing stock levels in order to enhance the performance of their maintenance logistics.

  3. 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

  4. 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.

  5. Forecast Jointed Rock Mass Compressive Strength Using a Numerical Model

    Directory of Open Access Journals (Sweden)

    Protosenya Anatoliy

    2016-01-01

    Full Text Available The method of forecasting the strength of the jointed rock mass by numerical modeling of finite element method in ABAQUS was described. The paper presents advantages of this method to solve the problem of determining the mechanical characteristics of jointed rock mass and the basic steps of creating a numerical geomechanical model of jointed rock mass and numerical experiment. Numerical simulation was carried out with jointed rock mass in order to obtain the ratio of strain and stress while loading the numerical model, determining parameters of quantitative assessment of the impact of the discontinuities orientation on the value of the compressive strength, compressive strength anisotropy. The results of the numerical experiment are compared with the data of experimental studies investigations. Innovative materials and structures are analyzed in this paper. The results that were obtained by calculation show qualitative agreement with the results of laboratory experiments of jointed rock mass.

  6. 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.

  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. Short period forecasting of catchment-scale precipitation. Part II: a water-balance storm model for short-term rainfall and flood forecasting

    Directory of Open Access Journals (Sweden)

    V. A. Bell

    2000-01-01

    Full Text Available A simple two-dimensional rainfall model, based on advection and conservation of mass in a vertical cloud column, is investigated for use in short-term rainfall and flood forecasting at the catchment scale under UK conditions. The model is capable of assimilating weather radar, satellite infra-red and surface weather observations, together with forecasts from a mesoscale numerical weather prediction model, to obtain frequently updated forecasts of rainfall fields. Such data assimilation helps compensate for the simplified model dynamics and, taken together, provides a practical real-time forecasting scheme for catchment scale applications. Various ways are explored for using information from a numerical weather prediction model (16.8 km grid within the higher resolution model (5 km grid. A number of model variants is considered, ranging from simple persistence and advection methods used as a baseline, to different forms of the dynamic rainfall model. Model performance is assessed using data from the Wardon Hill radar in Dorset for two convective events, on 10 June 1993 and 16 July 1995, when thunderstorms occurred over southern Britain. The results show that (i a simple advection-type forecast may be improved upon by using multiscan radar data in place of data from the lowest scan, and (ii advected, steady-state predictions from the dynamic model, using 'inferred updraughts', provides the best performance overall. Updraught velocity is inferred at the forecast origin from the last two radar fields, using the mass-balance equation and associated data and is held constant over the forecast period. This inference model proves superior to the buoyancy parameterisation of updraught employed in the original formulation. A selection of the different rainfall forecasts is used as input to a catchment flow forecasting model, the IH PDM (Probability Distributed Moisture model, to assess their effect on flow forecast accuracy for the 135 km2 Brue catchment

  9. Improving the Model for Energy Consumption Load Demand Forecasting

    Science.gov (United States)

    Bunnoon, Pituk; Chalermyanont, Kusumal; Limsakul, Chusak

    This paper proposes an application of a filter method in preprocessing stage for mid-term load demand forecasting to improve electricity load forecasting and to guarantee satisfactory forecasting accuracy. Case study employs the historical electricity consumption demand data in Thailand which were recorded in the 12 years of 1997 through to 2007. The load demand forecasted value is used for unit commitment and fuel reserve planning in the power system. This method consists of a trend component and a cyclical component decomposed from the original load demand using the Hodrick-Prescott (HP) filter in the preprocessing stage and the forecasting of each component using Double Neural Networks (DNNs) in the forecasting stage. Experimental results show that with preprocessing before forecasting can predict the load demand better than that without preprocessing.

  10. 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...

  11. Hybrid models for hydrological forecasting: Integration of data-driven and conceptual modelling techniques

    NARCIS (Netherlands)

    Corzo Perez, G.A.

    2009-01-01

    This book presents the investigation of different architectures of integrating hydrological knowledge and models with data-driven models for the purpose of hydrological flow forecasting. The models resulting from such integration are referred to as hybrid models. The book addresses the following

  12. Hybrid models for hydrological forecasting : Integration of data-driven and conceptual modelling techniques

    NARCIS (Netherlands)

    Corzo Perez, G.A.

    2009-01-01

    This book presents the investigation of different architectures of integrating hydrological knowledge and models with data-driven models for the purpose of hydrological flow forecasting. The models resulting from such integration are referred to as hybrid models. The book addresses the following

  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. A Novel Hybrid BND-FOA-LSSVM Model for Electricity Price Forecasting

    Directory of Open Access Journals (Sweden)

    Weishang Guo

    2017-09-01

    Full Text Available Accurate electricity price forecasting plays an important role in the profits of electricity market participants and the healthy development of electricity market. However, the electricity price time series hold the characteristics of volatility and randomness, which make it quite hard to forecast electricity price accurately. In this paper, a novel hybrid model for electricity price forecasting was proposed combining Beveridge-Nelson decomposition (BND method, fruit fly optimization algorithm (FOA, and least square support vector machine (LSSVM model, namely BND-FOA-LSSVM model. Firstly, the original electricity price time series were decomposed into deterministic term, periodic term, and stochastic term by using BND model. Then, these three decomposed terms were forecasted by employing LSSVM model, respectively. Meanwhile, to improve the forecasting performance, a new swarm intelligence optimization algorithm FOA was used to automatically determine the optimal parameters of LSSVM model for deterministic term forecasting, periodic term forecasting, and stochastic term forecasting. Finally, the forecasting result of electricity price can be obtained by multiplying the forecasting values of these three terms. The results show the mean absolute percentage error (MAPE, root mean square error (RMSE and mean absolute error (MAE of the proposed BND-FOA-LSSVM model are respectively 3.48%, 11.18 Yuan/MWh and 9.95 Yuan/MWh, which are much smaller than that of LSSVM, BND-LSSVM, FOA-LSSVM, auto-regressive integrated moving average (ARIMA, and empirical mode decomposition (EMD-FOA-LSSVM models. The proposed BND-FOA-LSSVM model is effective and practical for electricity price forecasting, which can improve the electricity price forecasting accuracy.

  15. Data on photovoltaic power forecasting models for Mediterranean climate.

    Science.gov (United States)

    Malvoni, M; De Giorgi, M G; Congedo, P M

    2016-06-01

    The weather data have a relevant impact on the photovoltaic (PV) power forecast, furthermore the PV power prediction methods need the historical data as input. The data presented in this article concern measured values of ambient temperature, module temperature, solar radiation in a Mediterranean climate. Hourly samples of the PV output power of 960kWP system located in Southern Italy were supplied for more 500 days. The data sets, given in , were used in DOI: 10.1016/j.enconman.2015.04.078, M.G. De Giorgi, P.M. Congedo, M. Malvoni, D. Laforgia (2015) [1] to compare Artificial Neural Networks and Least Square Support Vector Machines. It was found that LS-SVM with Wavelet Decomposition (WD) outperforms ANN method. In DOI: 10.1016/j.energy.2016.04.020, M.G. De Giorgi, P.M. Congedo, M. Malvoni (2016) [2] the same data were used for comparing different strategies for multi-step ahead forecast based on the hybrid Group Method of Data Handling networks and Least Square Support Vector Machine. The predicted PV power values by three models were reported in .

  16. Regressional modeling and forecasting of economic growth for arkhangelsk region

    Directory of Open Access Journals (Sweden)

    Robert Mikhailovich Nizhegorodtsev

    2012-12-01

    Full Text Available The regression models of GRP, considering the impact of three main factors: investment in fixed assets, wages amount, and, importantly, the innovation factor –the expenditures for research and development, are constructed in this paper on the empirical data for Arkhangelsk region. That approach permits to evaluate explicitly the contribution of innovation to economic growth. Regression analysis is the main research instrument, all calculations areperformedin the Microsoft Excel. There were made meaningful conclusions regarding the potential of the region's GRP growth by various factors, including impacts of positive and negative time lags. Adequate and relevant models are the base for estimation and forecasting values of the dependent variable (GRP and evaluating their confidence intervals. The invented method of research can be used in factor assessment and prediction of regional economic growth, including growth by expectations.

  17. 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.

  18. 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

  19. The performance of composite forecast models of value-at-risk in the energy market

    International Nuclear Information System (INIS)

    Chiu, Yen-Chen; Chuang, I-Yuan; Lai, Jing-Yi

    2010-01-01

    This paper examines a comparative evaluation of the predictive performance of various Value-at-Risk (VaR) models in the energy market. This study extends the conventional research in literature, by proposing composite forecast models for applying to Brent and WTI crude oil prices. Forecasting techniques considered here include the EWMA, stable density, Kernel density, Hull and White, GARCH-GPD, plus composite forecasts from linearly combining two or more of the competing models above. Findings show Hull and White to be the most powerful approach for capturing downside risk in the energy market. Reasonable results are also available from carefully combining VaR forecasts. (author)

  20. The Forecasting Power of the Yield Curve, a Supervised Factor Model Approach

    DEFF Research Database (Denmark)

    Boldrini, Lorenzo; Hillebrand, Eric Tobias

    We study the forecast power of the yield curve for macroeconomic time series, such as consumer price index, personal consumption expenditures, producer price index, real disposable income, unemployment rate, and industrial production. We employ a state-space model in which the forecasting objective...... is included in the state vector. This amounts to an augmented dynamic factor model in which the factors (level, slope, and curvature of the yield curve) are supervised for the macroeconomic forecast target. In other words, the factors are informed about the dynamics of the forecast objective. The factor...... loadings have the Nelson and Siegel (1987) structure and we consider one forecast target at a time. We compare the forecasting performance of our specification to benchmark models such as principal components regression, partial least squares, and ARMA(p,q) processes. We use the yield curve data from G...

  1. 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.

  2. 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.

  3. Improving groundwater predictions utilizing seasonal precipitation forecasts from general circulation models forced with sea surface temperature forecasts

    Science.gov (United States)

    Almanaseer, Naser; Sankarasubramanian, A.; Bales, Jerad

    2014-01-01

    Recent studies have found a significant association between climatic variability and basin hydroclimatology, particularly groundwater levels, over the southeast United States. The research reported in this paper evaluates the potential in developing 6-month-ahead groundwater-level forecasts based on the precipitation forecasts from ECHAM 4.5 General Circulation Model Forced with Sea Surface Temperature forecasts. Ten groundwater wells and nine streamgauges from the USGS Groundwater Climate Response Network and Hydro-Climatic Data Network were selected to represent groundwater and surface water flows, respectively, having minimal anthropogenic influences within the Flint River Basin in Georgia, United States. The writers employ two low-dimensional models [principle component regression (PCR) and canonical correlation analysis (CCA)] for predicting groundwater and streamflow at both seasonal and monthly timescales. Three modeling schemes are considered at the beginning of January to predict winter (January, February, and March) and spring (April, May, and June) streamflow and groundwater for the selected sites within the Flint River Basin. The first scheme (model 1) is a null model and is developed using PCR for every streamflow and groundwater site using previous 3-month observations (October, November, and December) available at that particular site as predictors. Modeling schemes 2 and 3 are developed using PCR and CCA, respectively, to evaluate the role of precipitation forecasts in improving monthly and seasonal groundwater predictions. Modeling scheme 3, which employs a CCA approach, is developed for each site by considering observed groundwater levels from nearby sites as predictands. The performance of these three schemes is evaluated using two metrics (correlation coefficient and relative RMS error) by developing groundwater-level forecasts based on leave-five-out cross-validation. Results from the research reported in this paper show that using

  4. 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.

  5. 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.

  6. 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

  7. 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.

  8. 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...

  9. 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.

  10. Data on photovoltaic power forecasting models for Mediterranean climate

    Directory of Open Access Journals (Sweden)

    M. Malvoni

    2016-06-01

    The data sets, given in Supplementary material File 1, were used in DOI: 10.1016/j.enconman.2015.04.078, M.G. De Giorgi, P.M. Congedo, M. Malvoni, D. Laforgia (2015 [1] to compare Artificial Neural Networks and Least Square Support Vector Machines. It was found that LS-SVM with Wavelet Decomposition (WD outperforms ANN method. In DOI: 10.1016/j.energy.2016.04.020, M.G. De Giorgi, P.M. Congedo, M. Malvoni (2016 [2] the same data were used for comparing different strategies for multi-step ahead forecast based on the hybrid Group Method of Data Handling networks and Least Square Support Vector Machine. The predicted PV power values by three models were reported in Supplementary material File 2.

  11. 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.

  12. Short-Term Power Forecasting Model for Photovoltaic Plants Based on Historical Similarity

    Directory of Open Access Journals (Sweden)

    M. Sonia Terreros-Olarte

    2013-05-01

    Full Text Available This paper proposes a new model for short-term forecasting of electric energy production in a photovoltaic (PV plant. The model is called HIstorical SImilar MIning (HISIMI model; its final structure is optimized by using a genetic algorithm, based on data mining techniques applied to historical cases composed by past forecasted values of weather variables, obtained from numerical tools for weather prediction, and by past production of electric power in a PV plant. The HISIMI model is able to supply spot values of power forecasts, and also the uncertainty, or probabilities, associated with those spot values, providing new useful information to users with respect to traditional forecasting models for PV plants. Such probabilities enable analysis and evaluation of risk associated with those spot forecasts, for example, in offers of energy sale for electricity markets. The results of spot forecasting of an illustrative example obtained with the HISIMI model for a real-life grid-connected PV plant, which shows high intra-hour variability of its actual power output, with forecasting horizons covering the following day, have improved those obtained with other two power spot forecasting models, which are a persistence model and an artificial neural network model.

  13. 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.

  14. 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

  15. A Generalized Martingale Model of Streamflow Forecast Uncertainty Evolution and its Application in the Three Gorge Reservoir Operation

    Science.gov (United States)

    Zhao, J.; Zhao, T.

    2012-12-01

    Streamflow forecasts are dynamically updated in real-time, which leads to a process of forecast uncertainty evolution. Generally, forecast uncertainty reduces as time progresses and more hydrologic information becomes available. This process of forecasting and uncertainty updating can be described by the martingale model of forecast evolution (MMFE), which formulates the total forecast uncertainty of streamflow in one future period as the sum of forecast improvements in the intermediate periods. This study tests the basic assumptions of MMFE with the streamflow forecast data from the Three Gorge Reservoir and shows that 1) real-world forecasts can be biased and tend to underestimate the actual streamflow and 2) real-world forecast uncertainty can be non-Gaussian and heavy-tailed. Based on these statistical tests, this study incorporates the normal quantile transform (NQT) method and issues a generalized NQT-MMFE model to simulate biased and non-Gaussian forecast uncertainties. The simulated streamflow forecast is similar to the real-world forecast in terms of NSE, MAE, and RMSE, which illustrates the effectiveness of the NQT-MMFE model. The simulated forecasts are further applied to a Monte-Carlo experiment of the Three Gorge Reservoir re-operation. The results illustrate that NQT-MMFE model within a rolling horizon decision making framework can efficiently exploit forecast information and make more robust decisions. The real-time streamflow forecast of TGR in 2008

  16. 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.

  17. Recovery boiler model; Soodakattilan kehitystyoe III

    Energy Technology Data Exchange (ETDEWEB)

    Janka, K.; Ylitalo, M.; Sundstroem, K.; Helke, R.; Heinola, M. [Kvaerner Pulping Oy, Tampere (Finland)

    1997-10-01

    The recovery boiler model was further tested and developed. At this moment the model includes submodels for: droplet drying, pyrolysis, char burning, gas burning and for droplet trajectory. During 1996 the formation of CH{sub 4} during pyrolysis and release of sulfur was included to the model. Further the formation of NO from fuel nitrogen and formation of thermal- NO were included to the model using Arrhenius type reaction rate equations. The calculated results are realistic and the model is used as a tool to find out methods to increase the efficiency and availability and decrease the emissions. Analysing the results of the earlier field study of 8 boilers showed that the furnace heat load, fuming rate, find the black liquor composition have influence on the enrichment of the potassium to the fly ash. (orig.)

  18. Extreme precipitation forecasting in the Chilean Andean region with complex topography using the Weather Research and Forecasting (WRF) model

    Science.gov (United States)

    Gironás, J.; Yáñez Morroni, G.; Caneo, M.; Delgado, R.

    2017-12-01

    The Weather Research and Forecasting (WRF) model is broadly used for weather forecasting, hindcasting and researching due to its good performance. However, the atmospheric conditions for simulating are not always optimal when it includes complex topographies: affecting WRF mathematical stability and convergence, therefore, its performance. As Chile is a country strongly characterized by a complex topography and high gradients of elevation, WRF is ineffective resolving Chilean mountainous terrain and foothills. The need to own an effective weather forecasting tool relies on that Chile's main cities are located in these regions. Furthermore, the most intense rainfall events take place here, commonly caused by the presence of cutoff lows. This work analyzes a microphysics scheme ensemble to enhance initial forecasts made by the Chilean Weather Agency (DMC). These forecasts were made over the Santiago piedmont, in Quebrada de Ramón watershed, located upstream an urban area highly populated. In this region a non-existing planning increases the potential damage of a flash flood. An initial testing was made over different vertical levels resolution (39 and 50 levels), and subsequently testing with land use and surface models, and finally with the initial and boundary condition data (GFS/FNL). Our task made emphasis in analyzing microphysics and lead time (3 to 5 days before the storm peak) in the computational simulations over three extreme rainfall events between 2015 and 2017. WRF shortcoming are also related to the complex configuration of the synoptic events, even when the steep topography difficult the rainfall event peak amount, and to a lesser degree, the exact rainfall event beginning prediction. No evident trend was found in the lead time, but as expected, better results in rainfall and zero isotherm height are obtained with smaller anticipation. We found that WRF do predict properly the N-hours with the biggest amount of rainfall (5 hours corresponding to

  19. 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.

  20. Comparison of Dst Forecast Models for Intense Geomagnetic Storms

    Science.gov (United States)

    Ji, Eun-Young; Moon, Y.-J.; Gopalswamy, N.; Lee, D.-H.

    2012-01-01

    We have compared six disturbance storm time (Dst) forecast models using 63 intense geomagnetic storms (Dst Dst data and the predicted Dst during the geomagnetic storm period as well as the difference of the value of minimum Dst (Delta Dst(sub min)) and the difference in the absolute value of Dst minimum time (Delta t(sub Dst)) between the observed and the predicted. As a result, we found that the model by Temerin and Li gives the best prediction for all parameters when all 63 events are considered. The model gives the average values: the linear correlation coefficient of 0.94, the RMS error of 14.8 nT, the Delta Dst(sub min) of 7.7 nT, and the absolute value of Delta t(sub Dst) of 1.5 hour. For further comparison, we classified the storm events into two groups according to the magnitude of Dst. We found that the model of Temerin and Lee is better than the other models for the events having 100 Dst Dst <= 200 nT.

  1. Short-Term Wind Speed Hybrid Forecasting Model Based on Bias Correcting Study and Its Application

    OpenAIRE

    Mingfei Niu; Shaolong Sun; Jie Wu; Yuanlei Zhang

    2015-01-01

    The accuracy of wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. In particular, reliable short-term wind speed forecasting can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, due to the strong stochastic nature and dynamic uncertainty of wind speed, the forecasting of wind speed data using different patterns is difficult. This paper proposes a novel combination bias c...

  2. A Novel Hybrid BND-FOA-LSSVM Model for Electricity Price Forecasting

    OpenAIRE

    Weishang Guo; Zhenyu Zhao

    2017-01-01

    Accurate electricity price forecasting plays an important role in the profits of electricity market participants and the healthy development of electricity market. However, the electricity price time series hold the characteristics of volatility and randomness, which make it quite hard to forecast electricity price accurately. In this paper, a novel hybrid model for electricity price forecasting was proposed combining Beveridge-Nelson decomposition (BND) method, fruit fly optimization algorit...

  3. Nowcasting of precipitation – Advective statistical forecast model (SAM) for the Czech Republic

    Czech Academy of Sciences Publication Activity Database

    Sokol, Zbyněk; Pešice, Petr

    2012-01-01

    Roč. 103, - (2012), s. 70-79 ISSN 0169-8095 R&D Projects: GA MŠk ME09033; GA ČR GA205/07/0905 Institutional research plan: CEZ:AV0Z30420517 Keywords : Precipitation forecast * Statistical models * Regression * Quantitative precipitation forecast * Extrapolation forecast Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 2.200, year: 2012 http://dx.doi.org/10.1016/j.atmosres.2011.07.013

  4. A Multi-scale, Multi-Model, Machine-Learning Solar Forecasting Technology

    Energy Technology Data Exchange (ETDEWEB)

    Hamann, Hendrik F. [IBM, Yorktown Heights, NY (United States). Thomas J. Watson Research Center

    2017-05-31

    The goal of the project was the development and demonstration of a significantly improved solar forecasting technology (short: Watt-sun), which leverages new big data processing technologies and machine-learnt blending between different models and forecast systems. The technology aimed demonstrating major advances in accuracy as measured by existing and new metrics which themselves were developed as part of this project. Finally, the team worked with Independent System Operators (ISOs) and utilities to integrate the forecasts into their operations.

  5. 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.

  6. Modeling Markov Switching ARMA-GARCH Neural Networks Models and an Application to Forecasting Stock Returns

    Directory of Open Access Journals (Sweden)

    Melike Bildirici

    2014-01-01

    Full Text Available The study has two aims. The first aim is to propose a family of nonlinear GARCH models that incorporate fractional integration and asymmetric power properties to MS-GARCH processes. The second purpose of the study is to augment the MS-GARCH type models with artificial neural networks to benefit from the universal approximation properties to achieve improved forecasting accuracy. Therefore, the proposed Markov-switching MS-ARMA-FIGARCH, APGARCH, and FIAPGARCH processes are further augmented with MLP, Recurrent NN, and Hybrid NN type neural networks. The MS-ARMA-GARCH family and MS-ARMA-GARCH-NN family are utilized for modeling the daily stock returns in an emerging market, the Istanbul Stock Index (ISE100. Forecast accuracy is evaluated in terms of MAE, MSE, and RMSE error criteria and Diebold-Mariano equal forecast accuracy tests. The results suggest that the fractionally integrated and asymmetric power counterparts of Gray’s MS-GARCH model provided promising results, while the best results are obtained for their neural network based counterparts. Further, among the models analyzed, the models based on the Hybrid-MLP and Recurrent-NN, the MS-ARMA-FIAPGARCH-HybridMLP, and MS-ARMA-FIAPGARCH-RNN provided the best forecast performances over the baseline single regime GARCH models and further, over the Gray’s MS-GARCH model. Therefore, the models are promising for various economic applications.

  7. A comparison of the VAR model and the PC factor model in forecasting inflation in Montenegro

    Directory of Open Access Journals (Sweden)

    Lipovina-Božović Milena

    2013-01-01

    Full Text Available Montenegro started using the euro in 2002 and regained independence in 2006. Its main economic partners are European countries, yet inflation movements in Montenegro do not coincide with consumer price fluctuations in the eurozone. Trying to develop a useful forecasting model for Montenegrin inflation, we compare the results of a three-variable vector autoregression (VAR model, and a principle component (PC factor model starting with twelve variables. The estimation period is January 2001 to December 2012, and the control months are the first six months of 2013. The results show that in forecasting inflation, despite a high level of Montenegrin economic dependence on international developments, more reliable forecasts are achieved with the use of additional information on a larger number of factors, which includes domestic economic activity.

  8. Markov Chain Modelling for Short-Term NDVI Time Series Forecasting

    Directory of Open Access Journals (Sweden)

    Stepčenko Artūrs

    2016-12-01

    Full Text Available In this paper, the NDVI time series forecasting model has been developed based on the use of discrete time, continuous state Markov chain of suitable order. The normalised difference vegetation index (NDVI is an indicator that describes the amount of chlorophyll (the green mass and shows the relative density and health of vegetation; therefore, it is an important variable for vegetation forecasting. A Markov chain is a stochastic process that consists of a state space. This stochastic process undergoes transitions from one state to another in the state space with some probabilities. A Markov chain forecast model is flexible in accommodating various forecast assumptions and structures. The present paper discusses the considerations and techniques in building a Markov chain forecast model at each step. Continuous state Markov chain model is analytically described. Finally, the application of the proposed Markov chain model is illustrated with reference to a set of NDVI time series data.

  9. Mountain range specific analog weather forecast model for ...

    Indian Academy of Sciences (India)

    various road management activities and for better assessment of avalanche danger situation during the winter period (November to April). Weather forecasting in the context of avalanche forecasting can be viewed as an initial essential process to begin assessment of an avalanche danger situation for a given area and ...

  10. Mountain range specific analog weather forecast model for ...

    Indian Academy of Sciences (India)

    hazard evaluation; Canadian Geotechnical Journal 7. 414–419. Sharma S S and Ganju A 2000 Complexities of avalanche forecasting in Western Himalaya: An overview; Cold. Regions Science and Technology 31 95–102. Singh A and Ganju A 2004 A supplement to nearest neigh- bour method for avalanche forecasting; ...

  11. Forecasting electricity spot-prices using linear univariate time-series models

    International Nuclear Information System (INIS)

    Cuaresma, Jesus Crespo; Hlouskova, Jaroslava; Kossmeier, Stephan; Obersteiner, Michael

    2004-01-01

    This paper studies the forecasting abilities of a battery of univariate models on hourly electricity spot prices, using data from the Leipzig Power Exchange. The specifications studied include autoregressive models, autoregressive-moving average models and unobserved component models. The results show that specifications, where each hour of the day is modelled separately present uniformly better forecasting properties than specifications for the whole time-series, and that the inclusion of simple probabilistic processes for the arrival of extreme price events can lead to improvements in the forecasting abilities of univariate models for electricity spot prices. (Author)

  12. Development of Seasonal ARIMA Models for Traffic Noise Forecasting

    Directory of Open Access Journals (Sweden)

    Guarnaccia Claudio

    2017-01-01

    Full Text Available In this paper, a time series analysis approach is adopted to monitor and predict a traffic noise levels dataset, measured in a site of Messina, Italy. In general, acoustical noise shows a high prediction complexity, since its slope is strongly related to the variability of the sources and to intrinsic randomness. In the analysed site the predominant source is road traffic, that has a periodic and non-stationary behaviour. The study of the time evolution of this hazardous agent is very useful to assess the impact to human health and activities. The time series models adopted in this paper are of the stochastic seasonal ARIMA class; these types of model are based on the strong periodicity registered in the acoustical equivalent levels. The observed periodicity is related to the highly variability of urban traffic in the different days of the week. Three different seasonal ARIMA models are proposed and calibrated on a rich dataset of 800 sound level measurements. The predictive capabilities of these techniques are encouraging. The implemented models show a good forecasting performances in terms of low residuals, i.e. difference between observed and estimated noise values. The residuals are analysed by means of statistical indexes, plots and tests.

  13. Short term load forecasting technique based on the seasonal exponential adjustment method and the regression model

    International Nuclear Information System (INIS)

    Wu, Jie; Wang, Jianzhou; Lu, Haiyan; Dong, Yao; Lu, Xiaoxiao

    2013-01-01

    Highlights: ► The seasonal and trend items of the data series are forecasted separately. ► Seasonal item in the data series is verified by the Kendall τ correlation testing. ► Different regression models are applied to the trend item forecasting. ► We examine the superiority of the combined models by the quartile value comparison. ► Paired-sample T test is utilized to confirm the superiority of the combined models. - Abstract: For an energy-limited economy system, it is crucial to forecast load demand accurately. This paper devotes to 1-week-ahead daily load forecasting approach in which load demand series are predicted by employing the information of days before being similar to that of the forecast day. As well as in many nonlinear systems, seasonal item and trend item are coexisting in load demand datasets. In this paper, the existing of the seasonal item in the load demand data series is firstly verified according to the Kendall τ correlation testing method. Then in the belief of the separate forecasting to the seasonal item and the trend item would improve the forecasting accuracy, hybrid models by combining seasonal exponential adjustment method (SEAM) with the regression methods are proposed in this paper, where SEAM and the regression models are employed to seasonal and trend items forecasting respectively. Comparisons of the quartile values as well as the mean absolute percentage error values demonstrate this forecasting technique can significantly improve the accuracy though models applied to the trend item forecasting are eleven different ones. This superior performance of this separate forecasting technique is further confirmed by the paired-sample T tests

  14. Short-Term Forecasting Models for Photovoltaic Plants: Analytical versus Soft-Computing Techniques

    Directory of Open Access Journals (Sweden)

    Claudio Monteiro

    2013-01-01

    Full Text Available We present and compare two short-term statistical forecasting models for hourly average electric power production forecasts of photovoltaic (PV plants: the analytical PV power forecasting model (APVF and the multiplayer perceptron PV forecasting model (MPVF. Both models use forecasts from numerical weather prediction (NWP tools at the location of the PV plant as well as the past recorded values of PV hourly electric power production. The APVF model consists of an original modeling for adjusting irradiation data of clear sky by an irradiation attenuation index, combined with a PV power production attenuation index. The MPVF model consists of an artificial neural network based model (selected among a large set of ANN optimized with genetic algorithms, GAs. The two models use forecasts from the same NWP tool as inputs. The APVF and MPVF models have been applied to a real-life case study of a grid-connected PV plant using the same data. Despite the fact that both models are quite different, they achieve very similar results, with forecast horizons covering all the daylight hours of the following day, which give a good perspective of their applicability for PV electric production sale bids to electricity markets.

  15. Acidification and recovery at mountain lakes in Central Alps assessed by the MAGIC model

    Directory of Open Access Journals (Sweden)

    Michela ROGORA

    2004-02-01

    Full Text Available The dynamic model MAGIC was calibrated and applied to 84 lakes in Central Alps to predict the response of water chemistry to different scenarios of atmospheric deposition of S and N compounds. Selected lakes were representative of a wide range of chemical characteristics and of sensitivity to acidification. The most sensitive lakes have already shown in the latest years signs of recovery in terms of pH and ANC. The model well captured the main trends in lake chemical data. According to the model forecast, recovery at sensitive lakes will continue in the next decades under the hypothesis of a further decrease of acidic input from the atmosphere. Results clearly demonstrated the benefits of achieving the emission reductions in both S and N compounds agreed under the Gothenburg Protocol. Nevertheless, besides the achieved reduction of SO4 2- deposition from the peak levels of the 80s, also N deposition should be reduced in the near future to protect alpine lakes from further acidification. The condition of lake catchments with regard to N saturation will probably be the dominant factor driving recovery extent. Beside atmospheric deposition, other factors proved to be important in determining long-term changes in surface water chemistry. Climate warming in particular affects weathering processes in lake catchments and dynamics of the N cycle. Including other factors specific to the alpine area, such as dust deposition and climate change, may improve the fit of experimental data by the model and the reliability of model forecast.

  16. Forecasting wind power production from a wind farm using the RAMS model

    DEFF Research Database (Denmark)

    Tiriolo, L.; Torcasio, R. C.; Montesanti, S.

    2015-01-01

    The importance of wind power forecast is commonly recognized because it represents a useful tool for grid integration and facilitates the energy trading. This work considers an example of power forecast for a wind farm in the Apennines in Central Italy. The orography around the site is complex...... and the horizontal resolution of the wind forecast has an important role. To explore this point we compared the performance of two 48 h wind power forecasts using the winds predicted by the Regional Atmospheric Modeling System (RAMS) for the year 2011. The two forecasts differ only for the horizontal resolution...... of the ECMWF Integrated Forecasting System (IFS), whose horizontal resolution over Central Italy is about 25 km at the time considered in this paper. Because wind observations were not available for the site, the power curve for the whole wind farm was derived from the ECMWF wind operational analyses available...

  17. Improving High-resolution Weather Forecasts using the Weather Research and Forecasting (WRF) Model with Upgraded Kain-Fritsch Cumulus Scheme

    Science.gov (United States)

    High-resolution weather forecasting is affected by many aspects, i.e. model initial conditions, subgrid-scale cumulus convection and cloud microphysics schemes. Recent 12km grid studies using the Weather Research and Forecasting (WRF) model have identified the importance of inco...

  18. Short-range forecasting and nowcasting, using a simple, isentropic prediction model

    Science.gov (United States)

    Petersen, Ralph A.; Homan, Jeffrey H.

    1989-01-01

    A numerical advection model which can be run on a local computer in a real-time forecast environment is described. This isentropic forecast model provides the local forecast office with easy access to temporally and spatially detailed estimates of atmospheric temperature, moisture, and wind field changes between 12-h rawinsonde observations. Case studies are presented based on the use of the model to predict the preconvective environment in spring and summer situations. Short-term forecasts of midtropospheric static stability patterns and stability index changes are traced for several severe storm events with and without the inclusion of surface data observed during the day. Forecast images of VAS low- and midlevel moisture fields and vertical moisture gradients are compared with the observations to determine the utility of the combined model/VAS imagery as a nowcasting guide.

  19. 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

    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...... estimation of the parameters of the models has been set up in order to provide a framework that can be applied easily in an operational context. A comprehensive testing procedure has been used to assess both point forecasts and probabilistic forecasts. Using only the past records of the solar irradiance......, the proposed model is able to perform point forecasts as accurately as other methods based on machine learning techniques. Moreover, the recursive ARMA-GARCH model is easier to set-up and it gives additional information about the uncertainty of the forecasts. Even if some strong assumption has been made...

  20. Different Models for Forecasting Wind Power Generation: Case Study

    Directory of Open Access Journals (Sweden)

    David Barbosa de Alencar

    2017-11-01

    Full Text Available Generation of electric energy through wind turbines is one of the practically inexhaustible alternatives of generation. It is considered a source of clean energy, but still needs a lot of research for the development of science and technologies that ensures uniformity in generation, providing a greater participation of this source in the energy matrix, since the wind presents abrupt variations in speed, density and other important variables. In wind-based electrical systems, it is essential to predict at least one day in advance the future values of wind behavior, in order to evaluate the availability of energy for the next period, which is relevant information in the dispatch of the generating units and in the control of the electrical system. This paper develops ultra-short, short, medium and long-term prediction models of wind speed, based on computational intelligence techniques, using artificial neural network models, Autoregressive Integrated Moving Average (ARIMA and hybrid models including forecasting using wavelets. For the application of the methodology, the meteorological variables of the database of the national organization system of environmental data (SONDA, Petrolina station, from 1 January 2004 to 31 March 2017, were used. A comparison among results by different used approaches is also done and it is also predicted the possibility of power and energy generation using a certain kind of wind generator.

  1. Weather forecasting for Eastern Amazon with OLAM model

    Directory of Open Access Journals (Sweden)

    Renato Ramos da Silva

    2014-12-01

    Full Text Available The OLAM model has as its characteristics the advantage to represent simultaneously the global and regional meteorological phenomena using the application of a grid refinement scheme. During the REMAM project the model was applied for a few case studies to evaluate its performance on numerical weather prediction for the eastern Amazon region. Case studies were performed for the twelve months of the year of 2009. The model results for those numerical experiments were compared with the observed data for the region of study. Precipitation data analysis showed that OLAM is able to represent the average mean accumulated precipitation and the seasonal features of the events occurrence, but can't predict the local total amount of precipitation. However, individual evaluation for a few cases had shown that OLAM was able to represent the dynamics and forecast a few days in advance the development of coastal meteorological systems such as the squall lines that are one of the most important precipitating systems of the Amazon.

  2. An Hourly Streamflow Forecasting Model Coupled with an Enforced Learning Strategy

    Directory of Open Access Journals (Sweden)

    Ming-Chang Wu

    2015-10-01

    Full Text Available Floods, one of the most significant natural hazards, often result in loss of life and property. Accurate hourly streamflow forecasting is always a key issue in hydrology for flood hazard mitigation. To improve the performance of hourly streamflow forecasting, a methodology concerning the development of neural network (NN based models with an enforced learning strategy is proposed in this paper. Firstly, four different NNs, namely back propagation network (BPN, radial basis function network (RBFN, self-organizing map (SOM, and support vector machine (SVM, are used to construct streamflow forecasting models. Through the cross-validation test, NN-based models with superior performance in streamflow forecasting are detected. Then, an enforced learning strategy is developed to further improve the performance of the superior NN-based models, i.e., SOM and SVM in this study. Finally, the proposed flow forecasting model is obtained. Actual applications are conducted to demonstrate the potential of the proposed model. Moreover, comparison between the NN-based models with and without the enforced learning strategy is performed to evaluate the effect of the enforced learning strategy on model performance. The results indicate that the NN-based models with the enforced learning strategy indeed improve the accuracy of hourly streamflow forecasting. Hence, the presented methodology is expected to be helpful for developing improved NN-based streamflow forecasting models.

  3. Refined Diebold-Mariano Test Methods for the Evaluation of Wind Power Forecasting Models

    Directory of Open Access Journals (Sweden)

    Hao Chen

    2014-07-01

    Full Text Available The scientific evaluation methodology for the forecast accuracy of wind power forecasting models is an important issue in the domain of wind power forecasting. However, traditional forecast evaluation criteria, such as Mean Squared Error (MSE and Mean Absolute Error (MAE, have limitations in application to some degree. In this paper, a modern evaluation criterion, the Diebold-Mariano (DM test, is introduced. The DM test can discriminate the significant differences of forecasting accuracy between different models based on the scheme of quantitative analysis. Furthermore, the augmented DM test with rolling windows approach is proposed to give a more strict forecasting evaluation. By extending the loss function to an asymmetric structure, the asymmetric DM test is proposed. Case study indicates that the evaluation criteria based on DM test can relieve the influence of random sample disturbance. Moreover, the proposed augmented DM test can provide more evidence when the cost of changing models is expensive, and the proposed asymmetric DM test can add in the asymmetric factor, and provide practical evaluation of wind power forecasting models. It is concluded that the two refined DM tests can provide reference to the comprehensive evaluation for wind power forecasting models.

  4. A hierarchical bayesian model to quantify uncertainty of stream water temperature forecasts.

    Directory of Open Access Journals (Sweden)

    Guillaume Bal

    Full Text Available Providing generic and cost effective modelling approaches to reconstruct and forecast freshwater temperature using predictors as air temperature and water discharge is a prerequisite to understanding ecological processes underlying the impact of water temperature and of global warming on continental aquatic ecosystems. Using air temperature as a simple linear predictor of water temperature can lead to significant bias in forecasts as it does not disentangle seasonality and long term trends in the signal. Here, we develop an alternative approach based on hierarchical Bayesian statistical time series modelling of water temperature, air temperature and water discharge using seasonal sinusoidal periodic signals and time varying means and amplitudes. Fitting and forecasting performances of this approach are compared with that of simple linear regression between water and air temperatures using i an emotive simulated example, ii application to three French coastal streams with contrasting bio-geographical conditions and sizes. The time series modelling approach better fit data and does not exhibit forecasting bias in long term trends contrary to the linear regression. This new model also allows for more accurate forecasts of water temperature than linear regression together with a fair assessment of the uncertainty around forecasting. Warming of water temperature forecast by our hierarchical Bayesian model was slower and more uncertain than that expected with the classical regression approach. These new forecasts are in a form that is readily usable in further ecological analyses and will allow weighting of outcomes from different scenarios to manage climate change impacts on freshwater wildlife.

  5. Stationarity test with a direct test for heteroskedasticity in exchange rate forecasting models

    Science.gov (United States)

    Khin, Aye Aye; Chau, Wong Hong; Seong, Lim Chee; Bin, Raymond Ling Leh; Teng, Kevin Low Lock

    2017-05-01

    Global economic has been decreasing in the recent years, manifested by the greater exchange rates volatility on international commodity market. This study attempts to analyze some prominent exchange rate forecasting models on Malaysian commodity trading: univariate ARIMA, ARCH and GARCH models in conjunction with stationarity test on residual diagnosis direct testing of heteroskedasticity. All forecasting models utilized the monthly data from 1990 to 2015. Given a total of 312 observations, the data used to forecast both short-term and long-term exchange rate. The forecasting power statistics suggested that the forecasting performance of ARIMA (1, 1, 1) model is more efficient than the ARCH (1) and GARCH (1, 1) models. For ex-post forecast, exchange rate was increased from RM 3.50 per USD in January 2015 to RM 4.47 per USD in December 2015 based on the baseline data. For short-term ex-ante forecast, the analysis results indicate a decrease in exchange rate on 2016 June (RM 4.27 per USD) as compared with 2015 December. A more appropriate forecasting method of exchange rate is vital to aid the decision-making process and planning on the sustainable commodities' production in the world economy.

  6. Study on network traffic forecast model of SVR optimized by GAFSA

    International Nuclear Information System (INIS)

    Liu, Yuan; Wang, RuiXue

    2016-01-01

    There are some problems, such as low precision, on existing network traffic forecast model. In accordance with these problems, this paper proposed the network traffic forecast model of support vector regression (SVR) algorithm optimized by global artificial fish swarm algorithm (GAFSA). GAFSA constitutes an improvement of artificial fish swarm algorithm, which is a swarm intelligence optimization algorithm with a significant effect of optimization. The optimum training parameters used for SVR could be calculated by optimizing chosen parameters, which would make the forecast more accurate. With the optimum training parameters searched by GAFSA algorithm, a model of network traffic forecast, which greatly solved problems of great errors in SVR improved by others intelligent algorithms, could be built with the forecast result approaching stability and the increased forecast precision. The simulation shows that, compared with other models (e.g. GA-SVR, CPSO-SVR), the forecast results of GAFSA-SVR network traffic forecast model is more stable with the precision improved to more than 89%, which plays an important role on instructing network control behavior and analyzing security situation.

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

    DEFF Research Database (Denmark)

    Halbleib, Roxana; Voev, Valeri

    2011-01-01

    positive definite, but biased covariance forecasts. In this paper, we provide empirical evidence that parsimonious versions of the model generate the best covariance forecasts in the absence of bias correction. Moreover, we show by means of stochastic dominance tests that any risk averse investor......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....

  8. Urban Saturated Power Load Analysis Based on a Novel Combined Forecasting Model

    Directory of Open Access Journals (Sweden)

    Huiru Zhao

    2015-03-01

    Full Text Available Analysis of urban saturated power loads is helpful to coordinate urban power grid construction and economic social development. There are two different kinds of forecasting models: the logistic curve model focuses on the growth law of the data itself, while the multi-dimensional forecasting model considers several influencing factors as the input variables. To improve forecasting performance, a novel combined forecasting model for saturated power load analysis was proposed in this paper, which combined the above two models. Meanwhile, the weights of these two models in the combined forecasting model were optimized by employing a fruit fly optimization algorithm. Using Hubei Province as the example, the effectiveness of the proposed combined forecasting model was verified, demonstrating a higher forecasting accuracy. The analysis result shows that the power load of Hubei Province will reach saturation in 2039, and the annual maximum power load will reach about 78,630 MW. The results obtained from this proposed hybrid urban saturated power load analysis model can serve as a reference for sustainable development for urban power grids, regional economies, and society at large.

  9. A working model of stroke recovery from rehabilitation robotics practitioners.

    Science.gov (United States)

    Krebs, Hermano Igo; Volpe, Bruce; Hogan, Neville

    2009-02-25

    We reviewed some of our initial insights about the process of upper-limb behavioral recovery following stroke. Evidence to date indicates that intensity, task specificity, active engagement, and focusing training on motor coordination are key factors enabling efficacious recovery. On modeling, experience with over 400 stroke patients has suggested a working model of recovery similar to implicit motor learning. Ultimately, we plan to apply these insights in the development of customized training paradigms to enhance recovery.

  10. Improved Mental Acuity Forecasting with an Individualized Quantitative Sleep Model

    Directory of Open Access Journals (Sweden)

    Brent D. Winslow

    2017-04-01

    Full Text Available Sleep impairment significantly alters human brain structure and cognitive function, but available evidence suggests that adults in developed nations are sleeping less. A growing body of research has sought to use sleep to forecast cognitive performance by modeling the relationship between the two, but has generally focused on vigilance rather than other cognitive constructs affected by sleep, such as reaction time, executive function, and working memory. Previous modeling efforts have also utilized subjective, self-reported sleep durations and were restricted to laboratory environments. In the current effort, we addressed these limitations by employing wearable systems and mobile applications to gather objective sleep information, assess multi-construct cognitive performance, and model/predict changes to mental acuity. Thirty participants were recruited for participation in the study, which lasted 1 week. Using the Fitbit Charge HR and a mobile version of the automated neuropsychological assessment metric called CogGauge, we gathered a series of features and utilized the unified model of performance to predict mental acuity based on sleep records. Our results suggest that individuals poorly rate their sleep duration, supporting the need for objective sleep metrics to model circadian changes to mental acuity. Participant compliance in using the wearable throughout the week and responding to the CogGauge assessments was 80%. Specific biases were identified in temporal metrics across mobile devices and operating systems and were excluded from the mental acuity metric development. Individualized prediction of mental acuity consistently outperformed group modeling. This effort indicates the feasibility of creating an individualized, mobile assessment and prediction of mental acuity, compatible with the majority of current mobile devices.

  11. Improved Mental Acuity Forecasting with an Individualized Quantitative Sleep Model.

    Science.gov (United States)

    Winslow, Brent D; Nguyen, Nam; Venta, Kimberly E

    2017-01-01

    Sleep impairment significantly alters human brain structure and cognitive function, but available evidence suggests that adults in developed nations are sleeping less. A growing body of research has sought to use sleep to forecast cognitive performance by modeling the relationship between the two, but has generally focused on vigilance rather than other cognitive constructs affected by sleep, such as reaction time, executive function, and working memory. Previous modeling efforts have also utilized subjective, self-reported sleep durations and were restricted to laboratory environments. In the current effort, we addressed these limitations by employing wearable systems and mobile applications to gather objective sleep information, assess multi-construct cognitive performance, and model/predict changes to mental acuity. Thirty participants were recruited for participation in the study, which lasted 1 week. Using the Fitbit Charge HR and a mobile version of the automated neuropsychological assessment metric called CogGauge, we gathered a series of features and utilized the unified model of performance to predict mental acuity based on sleep records. Our results suggest that individuals poorly rate their sleep duration, supporting the need for objective sleep metrics to model circadian changes to mental acuity. Participant compliance in using the wearable throughout the week and responding to the CogGauge assessments was 80%. Specific biases were identified in temporal metrics across mobile devices and operating systems and were excluded from the mental acuity metric development. Individualized prediction of mental acuity consistently outperformed group modeling. This effort indicates the feasibility of creating an individualized, mobile assessment and prediction of mental acuity, compatible with the majority of current mobile devices.

  12. Daily value-at-risk modeling and forecast evaluation: The realized volatility approach

    Directory of Open Access Journals (Sweden)

    Zhen Yao Wong

    2016-09-01

    Full Text Available One of the main applications of conditional volatility modeling and forecasting of financial assets is the value-at-risk (VaR estimation that is used by financial institutions for reporting the daily capital in risk. It remains a question on whether realized volatility (RV models that incorporate the use of intraday data produce better VaR forecasts compared to methodologies that are based solely on daily returns. This study provides extensive comparison of out-of-sample volatility and VaR forecast performance on three equity market indices: S&P500, FTSE100, and DAX30 using 13 risk models that consist of 5 GARCH specifications, 4 ARFIMAX specifications and 4 HARX specifications. The out-of-sample volatility forecasts are evaluated by various loss functions and simple scoring procedures in order to identity the model that produces the overall best volatility forecasts. For VaR forecasts, the models are evaluated using a two-stage backtesting procedure where the models undergo unconditional and conditional coverage tests to eliminate underperforming models and the qualified models are then evaluated using the quadratic probability score (QPS function that is computed based on various VaR loss functions. The results showed that RV models outperform GARCH models for volatility forecasts, but a simple EGARCH model outperforms the rest models for most of the VaR forecasts. The results also indicated that capturing the asymmetric behavior of volatility dynamics is essential for accurate volatility and VaR forecasts. The findings of this study provide useful information for market risk regulation, financial risk management and further investigations such as extension to derivative markets and options pricing.

  13. 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.

  14. Oregon Washington Coastal Ocean Forecast System: Real-time Modeling and Data Assimilation

    Science.gov (United States)

    Erofeeva, S.; Kurapov, A. L.; Pasmans, I.

    2016-02-01

    Three-day forecasts of ocean currents, temperature and salinity along the Oregon and Washington coasts are produced daily by a numerical ROMS-based ocean circulation model. NAM is used to derive atmospheric forcing for the model. Fresh water discharge from Columbia River, Fraser River, and small rivers in Puget Sound are included. The forecast is constrained by open boundary conditions derived from the global Navy HYCOM model and once in 3 days assimilation of recent data, including HF radar surface currents, sea surface temperature from the GOES satellite, and SSH from several satellite altimetry missions. 4-dimensional variational data assimilation is implemented in 3-day time windows using the tangent linear and adjoint codes developed at OSU. The system is semi-autonomous - all the data, including NAM and HYCOM fields are automatically updated, and daily operational forecast is automatically initiated. The pre-assimilation data quality control and post-assimilation forecast quality control require the operator's involvement. The daily forecast and 60 days of hindcast fields are available for public on opendap. As part of the system model validation plots to various satellites and SEAGLIDER are also automatically updated and available on the web (http://ingria.coas.oregonstate.edu/rtdavow/). Lessons learned in this pilot real-time coastal ocean forecasting project help develop and test metrics for forecast skill assessment for the West Coast Operational Forecast System (WCOFS), currently at testing and development phase at the National Oceanic and Atmospheric Administration (NOAA).

  15. Structural forecasts for the Danish economy using a dynamic AAGE model

    DEFF Research Database (Denmark)

    Adams, Philip D; Andersen, Lill Thanning; Jacobsen, Lars Bo

    We describe how an applied dynamic general equilibrium model of the Danish economy has been developed to generate structural forecasts. The forecasts provide a microeconomic picture that is consistent with a macroeconomic scenario and the other inputs. We provide an overview of the inputs require...

  16. BAYESIAN FORECASTS COMBINATION TO IMPROVE THE ROMANIAN INFLATION PREDICTIONS BASED ON ECONOMETRIC MODELS

    Directory of Open Access Journals (Sweden)

    Mihaela Simionescu

    2014-12-01

    Full Text Available There are many types of econometric models used in predicting the inflation rate, but in this study we used a Bayesian shrinkage combination approach. This methodology is used in order to improve the predictions accuracy by including information that is not captured by the econometric models. Therefore, experts’ forecasts are utilized as prior information, for Romania these predictions being provided by Institute for Economic Forecasting (Dobrescu macromodel, National Commission for Prognosis and European Commission. The empirical results for Romanian inflation show the superiority of a fixed effects model compared to other types of econometric models like VAR, Bayesian VAR, simultaneous equations model, dynamic model, log-linear model. The Bayesian combinations that used experts’ predictions as priors, when the shrinkage parameter tends to infinite, improved the accuracy of all forecasts based on individual models, outperforming also zero and equal weights predictions and naïve forecasts.

  17. Comparison of short term rainfall forecasts for model based flow prediction in urban drainage systems

    DEFF Research Database (Denmark)

    Thorndahl, Søren; Poulsen, Troels Sander; Bøvith, Thomas

    2012-01-01

    Forecast based flow prediction in drainage systems can be used to implement real time control of drainage systems. This study compares two different types of rainfall forecasts – a radar rainfall extrapolation based nowcast model and a numerical weather prediction model. The models are applied...... performance of the system is found using the radar nowcast for the short leadtimes and weather model for larger lead times....

  18. Comparison Of Short Term Rainfall Forecasts For Model Based Flow Prediction In Urban Drainage Systems

    DEFF Research Database (Denmark)

    Thorndahl, Søren Liedtke; Poulsen, Troels Sander; Bøvith, Thomas

    2012-01-01

    Forecast based flow prediction in drainage systems can be used to implement real time control of drainage systems. This study compares two different types of rainfall forecasts – a radar rainfall extrapolation based nowcast model and a numerical weather prediction model. The models are applied...... performance of the system is found using the radar nowcast for the short leadtimes and weather model for larger lead times....

  19. Improving real-time inflow forecasting into hydropower reservoirs through a complementary modelling framework

    Science.gov (United States)

    Gragne, A. S.; Sharma, A.; Mehrotra, R.; Alfredsen, K.

    2015-08-01

    Accuracy of reservoir inflow forecasts is instrumental for maximizing the value of water resources and benefits gained through hydropower generation. Improving hourly reservoir inflow forecasts over a 24 h lead time is considered within the day-ahead (Elspot) market of the Nordic exchange market. A complementary modelling framework presents an approach for improving real-time forecasting without needing to modify the pre-existing forecasting model, but instead formulating an independent additive or complementary model that captures the structure the existing operational model may be missing. We present here the application of this principle for issuing improved hourly inflow forecasts into hydropower reservoirs over extended lead times, and the parameter estimation procedure reformulated to deal with bias, persistence and heteroscedasticity. The procedure presented comprises an error model added on top of an unalterable constant parameter conceptual model. This procedure is applied in the 207 km2 Krinsvatn catchment in central Norway. The structure of the error model is established based on attributes of the residual time series from the conceptual model. Besides improving forecast skills of operational models, the approach estimates the uncertainty in the complementary model structure and produces probabilistic inflow forecasts that entrain suitable information for reducing uncertainty in the decision-making processes in hydropower systems operation. Deterministic and probabilistic evaluations revealed an overall significant improvement in forecast accuracy for lead times up to 17 h. Evaluation of the percentage of observations bracketed in the forecasted 95 % confidence interval indicated that the degree of success in containing 95 % of the observations varies across seasons and hydrologic years.

  20. Improving inflow forecasting into hydropower reservoirs through a complementary modelling framework

    Science.gov (United States)

    Gragne, A. S.; Sharma, A.; Mehrotra, R.; Alfredsen, K.

    2014-10-01

    Accuracy of reservoir inflow forecasts is instrumental for maximizing the value of water resources and benefits gained through hydropower generation. Improving hourly reservoir inflow forecasts over a 24 h lead-time is considered within the day-ahead (Elspot) market of the Nordic exchange market. We present here a new approach for issuing hourly reservoir inflow forecasts that aims to improve on existing forecasting models that are in place operationally, without needing to modify the pre-existing approach, but instead formulating an additive or complementary model that is independent and captures the structure the existing model may be missing. Besides improving forecast skills of operational models, the approach estimates the uncertainty in the complementary model structure and produces probabilistic inflow forecasts that entrain suitable information for reducing uncertainty in the decision-making processes in hydropower systems operation. The procedure presented comprises an error model added on top of an un-alterable constant parameter conceptual model, the models being demonstrated with reference to the 207 km2 Krinsvatn catchment in central Norway. The structure of the error model is established based on attributes of the residual time series from the conceptual model. Deterministic and probabilistic evaluations revealed an overall significant improvement in forecast accuracy for lead-times up to 17 h. Season based evaluations indicated that the improvement in inflow forecasts varies across seasons and inflow forecasts in autumn and spring are less successful with the 95% prediction interval bracketing less than 95% of the observations for lead-times beyond 17 h.

  1. Probabilistic Wind Speed Forecasting using Ensembles and Bayesian Model Averaging

    National Research Council Canada - National Science Library

    Sloughter, J. M; Gneiting, Tilmann; Raftery, Adrian E

    2008-01-01

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

  2. NOAA/NCEP Global Forecast System (GFS) Atmospheric Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — U.S. National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) numerical weather...

  3. Using a safety forecast model to calculate future safety metrics.

    Science.gov (United States)

    2017-05-01

    This research sought to identify a process to improve long-range planning prioritization by using forecasted : safety metrics in place of the existing Utah Department of Transportation Safety Indexa metric based on historical : crash data. The res...

  4. Mid-Term Energy Demand Forecasting by Hybrid Neuro-Fuzzy Models

    Directory of Open Access Journals (Sweden)

    Arash Miranian

    2011-12-01

    Full Text Available This paper proposes a structure for long-term energy demand forecasting. The proposed hybrid approach, called HPLLNF, uses the local linear neuro-fuzzy (LLNF model as the forecaster and utilizes the Hodrick–Prescott (HP filter for extraction of the trend and cyclic components of the energy demand series. Besides, the sophisticated technique of mutual information (MI is employed to select the most relevant input features with least possible redundancies for the forecast model. Each generated component by the HP filter is then modeled through an LLNF model. Starting from an optimal least square estimation, the local linear model tree (LOLIMOT learning algorithm increases the complexity of the LLNF model as long as its performance is improved. The proposed HPLLNF model with MI-based input selection is applied to the problem of long-term energy forecasting in three different case studies, including forecasting of the gasoline, crude oil and natural gas demand over the next 12 months. The obtained forecasting results reveal the noteworthy performance of the proposed approach for long-term energy demand forecasting applications.

  5. Using Bayesian Model Averaging (BMA) to calibrate probabilistic surface temperature forecasts over Iran

    Energy Technology Data Exchange (ETDEWEB)

    Soltanzadeh, I. [Tehran Univ. (Iran, Islamic Republic of). Inst. of Geophysics; Azadi, M.; Vakili, G.A. [Atmospheric Science and Meteorological Research Center (ASMERC), Teheran (Iran, Islamic Republic of)

    2011-07-01

    Using Bayesian Model Averaging (BMA), an attempt was made to obtain calibrated probabilistic numerical forecasts of 2-m temperature over Iran. The ensemble employs three limited area models (WRF, MM5 and HRM), with WRF used with five different configurations. Initial and boundary conditions for MM5 and WRF are obtained from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) and for HRM the initial and boundary conditions come from analysis of Global Model Europe (GME) of the German Weather Service. The resulting ensemble of seven members was run for a period of 6 months (from December 2008 to May 2009) over Iran. The 48-h raw ensemble outputs were calibrated using BMA technique for 120 days using a 40 days training sample of forecasts and relative verification data. The calibrated probabilistic forecasts were assessed using rank histogram and attribute diagrams. Results showed that application of BMA improved the reliability of the raw ensemble. Using the weighted ensemble mean forecast as a deterministic forecast it was found that the deterministic-style BMA forecasts performed usually better than the best member's deterministic forecast. (orig.)

  6. Using Bayesian Model Averaging (BMA to calibrate probabilistic surface temperature forecasts over Iran

    Directory of Open Access Journals (Sweden)

    I. Soltanzadeh

    2011-07-01

    Full Text Available Using Bayesian Model Averaging (BMA, an attempt was made to obtain calibrated probabilistic numerical forecasts of 2-m temperature over Iran. The ensemble employs three limited area models (WRF, MM5 and HRM, with WRF used with five different configurations. Initial and boundary conditions for MM5 and WRF are obtained from the National Centers for Environmental Prediction (NCEP Global Forecast System (GFS and for HRM the initial and boundary conditions come from analysis of Global Model Europe (GME of the German Weather Service. The resulting ensemble of seven members was run for a period of 6 months (from December 2008 to May 2009 over Iran. The 48-h raw ensemble outputs were calibrated using BMA technique for 120 days using a 40 days training sample of forecasts and relative verification data. The calibrated probabilistic forecasts were assessed using rank histogram and attribute diagrams. Results showed that application of BMA improved the reliability of the raw ensemble. Using the weighted ensemble mean forecast as a deterministic forecast it was found that the deterministic-style BMA forecasts performed usually better than the best member's deterministic forecast.

  7. 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

  8. Development of a High Resolution Weather Forecast Model for Mesoamerica Using the NASA Ames Code I Private Cloud Computing Environment

    Science.gov (United States)

    Molthan, Andrew; Case, Jonathan; Venner, Jason; Moreno-Madrinan, Max J.; Delgado, Francisco

    2012-01-01

    Two projects at NASA Marshall Space Flight Center have collaborated to develop a high resolution weather forecast model for Mesoamerica: The NASA Short-term Prediction Research and Transition (SPoRT) Center, which integrates unique NASA satellite and weather forecast modeling capabilities into the operational weather forecasting community. NASA's SERVIR Program, which integrates satellite observations, ground-based data, and forecast models to improve disaster response in Central America, the Caribbean, Africa, and the Himalayas.

  9. Characterizing and modeling the free recovery and constrained recovery behavior of a polyurethane shape memory polymer

    International Nuclear Information System (INIS)

    Volk, Brent L; Lagoudas, Dimitris C; Maitland, Duncan J

    2011-01-01

    In this work, tensile tests and one-dimensional constitutive modeling were performed on a high recovery force polyurethane shape memory polymer that is being considered for biomedical applications. The tensile tests investigated the free recovery (zero load) response as well as the constrained displacement recovery (stress recovery) response at extension values up to 25%, and two consecutive cycles were performed during each test. The material was observed to recover 100% of the applied deformation when heated at zero load in the second thermomechanical cycle, and a stress recovery of 1.5–4.2 MPa was observed for the constrained displacement recovery experiments. After the experiments were performed, the Chen and Lagoudas model was used to simulate and predict the experimental results. The material properties used in the constitutive model—namely the coefficients of thermal expansion, shear moduli, and frozen volume fraction—were calibrated from a single 10% extension free recovery experiment. The model was then used to predict the material response for the remaining free recovery and constrained displacement recovery experiments. The model predictions match well with the experimental data

  10. Hybrid model for forecasting time series with trend, seasonal and salendar variation patterns

    Science.gov (United States)

    Suhartono; Rahayu, S. P.; Prastyo, D. D.; Wijayanti, D. G. P.; Juliyanto

    2017-09-01

    Most of the monthly time series data in economics and business in Indonesia and other Moslem countries not only contain trend and seasonal, but also affected by two types of calendar variation effects, i.e. the effect of the number of working days or trading and holiday effects. The purpose of this research is to develop a hybrid model or a combination of several forecasting models to predict time series that contain trend, seasonal and calendar variation patterns. This hybrid model is a combination of classical models (namely time series regression and ARIMA model) and/or modern methods (artificial intelligence method, i.e. Artificial Neural Networks). A simulation study was used to show that the proposed procedure for building the hybrid model could work well for forecasting time series with trend, seasonal and calendar variation patterns. Furthermore, the proposed hybrid model is applied for forecasting real data, i.e. monthly data about inflow and outflow of currency at Bank Indonesia. The results show that the hybrid model tend to provide more accurate forecasts than individual forecasting models. Moreover, this result is also in line with the third results of the M3 competition, i.e. the hybrid model on average provides a more accurate forecast than the individual model.

  11. Modelling and forecasting monthly and daily river discharge data using hybrid models and considering autoregressive heteroscedasticity

    Science.gov (United States)

    Szolgayova, Elena

    2010-05-01

    Hybrid modelling, used for simulation and forecasting of hydrological time series, involving both process-based and data-driven types of models combines the available domain knowledge and process physics with the recent advances in data driven tools. In this way, complex hydrological processes can be modelled and forecasted by decomposing the problem into several smaller sub - problems and using process physics based models where these are most appropriate, and data dictated tools (such as ANN, time series models or traditional statistics) for the residual data, when necessary. The fitting and forecasting performance of such models have to be explored case based. So far, only a few attempts to apply various nonlinear time series models within such a framework were reported in the hydrological modelling literature. This contribution presents results concerning the possibility to use GARCH type of models for such purposes. More specifically, error time series from two hydrological conceptual models were analyzed (applied on time series measured from the Hron and Morava Rivers in Slovakia), concentrating on the improvement of the modelling and forecasting performance of these models. The goal of investigation was to try to expand the knowledge in the time series modelling of hydrological model error time series with the aim to test and develop appropriate methods for various time steps from the GARCH family of models. In order to achieve this, following steps were taken: 1. The presence of heteroscedasticity was verified in time series. 2. A model from the GARCH family was fitted on the data, comparing the fit with a fit of an ARMA model. 3. One - step - ahead forecasts from the fitted models were produced, performing comparisons. The investigation of model properties and performances was thoroughly tested under various conditions of their future practical applications. In general, heteroscedasticity was present in the majority of the error time series of the

  12. How uncertainty in socio-economic variables affects large-scale transport model forecasts

    DEFF Research Database (Denmark)

    Manzo, Stefano; Nielsen, Otto Anker; Prato, Carlo Giacomo

    2015-01-01

    time, especially with respect to large-scale transport models. The study described in this paper contributes to fill the gap by investigating the effects of uncertainty in socio-economic variables growth rate projections on large-scale transport model forecasts, using the Danish National Transport......A strategic task assigned to large-scale transport models is to forecast the demand for transport over long periods of time to assess transport projects. However, by modelling complex systems transport models have an inherent uncertainty which increases over time. As a consequence, the longer...... the period forecasted the less reliable is the forecasted model output. Describing uncertainty propagation patterns over time is therefore important in order to provide complete information to the decision makers. Among the existing literature only few studies analyze uncertainty propagation patterns over...

  13. Daily Crude Oil Price Forecasting Using Hybridizing Wavelet and Artificial Neural Network Model

    Directory of Open Access Journals (Sweden)

    Ani Shabri

    2014-01-01

    Full Text Available A new method based on integrating discrete wavelet transform and artificial neural networks (WANN model for daily crude oil price forecasting is proposed. The discrete Mallat wavelet transform is used to decompose the crude price series into one approximation series and some details series (DS. The new series obtained by adding the effective one approximation series and DS component is then used as input into the ANN model to forecast crude oil price. The relative performance of WANN model was compared to regular ANN model for crude oil forecasting at lead times of 1 day for two main crude oil price series, West Texas Intermediate (WTI and Brent crude oil spot prices. In both cases, WANN model was found to provide more accurate crude oil prices forecasts than individual ANN model.

  14. Residential Saudi load forecasting using analytical model and Artificial Neural Networks

    Science.gov (United States)

    Al-Harbi, Ahmad Abdulaziz

    In recent years, load forecasting has become one of the main fields of study and research. Short Term Load Forecasting (STLF) is an important part of electrical power system operation and planning. This work investigates the applicability of different approaches; Artificial Neural Networks (ANNs) and hybrid analytical models to forecast residential load in Kingdom of Saudi Arabia (KSA). These two techniques are based on model human modes behavior formulation. These human modes represent social, religious, official occasions and environmental parameters impact. The analysis is carried out on residential areas for three regions in two countries exposed to distinct people activities and weather conditions. The collected data are for Al-Khubar and Yanbu industrial city in KSA, in addition to Seattle, USA to show the validity of the proposed models applied on residential load. For each region, two models are proposed. First model is next hour load forecasting while second model is next day load forecasting. Both models are analyzed using the two techniques. The obtained results for ANN next hour models yield very accurate results for all areas while relatively reasonable results are achieved when using hybrid analytical model. For next day load forecasting, the two approaches yield satisfactory results. Comparative studies were conducted to prove the effectiveness of the models proposed.

  15. WALS Estimation and Forecasting in Factor-based Dynamic Models with an Application to Armenia

    OpenAIRE

    Poghosyan, Karen; Magnus, Jan R.

    2012-01-01

    Two model averaging approaches are used and compared in estimating and forecasting dynamic factor models, the well-known Bayesian model averaging (BMA) and the recently developed weighted average least squares (WALS). Both methods propose to combine frequentist estimators using Bayesian weights. We apply our framework to the Armenian economy using quarterly data from 20002010, and we estimate and forecast real GDP growth and inflation.

  16. Modeling and Forecasting of Water Demand in Isfahan Using Underlying Trend Concept and Time Series

    OpenAIRE

    H. Sadeghi; A. Akhondali; meisam haddad; M. Golabi

    2016-01-01

    Introduction: Accurate water demand modeling for the city is very important for forecasting and policies adoption related to water resources management. Thus, for future requirements of water estimation, forecasting and modeling, it is important to utilize models with little errors. Water has a special place among the basic human needs, because it not hampers human life. The importance of the issue of water management in the extraction and consumption, it is necessary as a basic need. Municip...

  17. A Comparison Study of Return Ratio-Based Academic Enrollment Forecasting Models. Professional File. Article 129, Spring 2013

    Science.gov (United States)

    Zan, Xinxing Anna; Yoon, Sang Won; Khasawneh, Mohammad; Srihari, Krishnaswami

    2013-01-01

    In an effort to develop a low-cost and user-friendly forecasting model to minimize forecasting error, we have applied average and exponentially weighted return ratios to project undergraduate student enrollment. We tested the proposed forecasting models with different sets of historical enrollment data, such as university-, school-, and…

  18. Using Satellite Data and Land Surface Models to Monitor and Forecast Drought Conditions in Africa and Middle East

    Science.gov (United States)

    Arsenault, K. R.; Shukla, S.; Getirana, A.; Peters-Lidard, C. D.; Kumar, S.; McNally, A.; Zaitchik, B. F.; Badr, H. S.; Funk, C. C.; Koster, R. D.; Narapusetty, B.; Jung, H. C.; Roningen, J. M.

    2017-12-01

    Drought and water scarcity are among the important issues facing several regions within Africa and the Middle East. In addition, these regions typically have sparse ground-based data networks, where sometimes remotely sensed observations may be the only data available. Long-term satellite records can help with determining historic and current drought conditions. In recent years, several new satellites have come on-line that monitor different hydrological variables, including soil moisture and terrestrial water storage. Though these recent data records may be considered too short for the use in identifying major droughts, they do provide additional information that can better characterize where water deficits may occur. We utilize recent satellite data records of Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) and the European Space Agency's Advanced Scatterometer (ASCAT) soil moisture retrievals. Combining these records with land surface models (LSMs), NASA's Catchment and the Noah Multi-Physics (MP), is aimed at improving the land model states and initialization for seasonal drought forecasts. The LSMs' total runoff is routed through the Hydrological Modeling and Analysis Platform (HyMAP) to simulate surface water dynamics, which can provide an additional means of validation against in situ streamflow data. The NASA Land Information System (LIS) software framework drives the LSMs and HyMAP and also supports the capability to assimilate these satellite retrievals, such as soil moisture and TWS. The LSMs are driven for 30+ years with NASA's Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and the USGS/UCSB Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) rainfall dataset. The seasonal water deficit forecasts are generated using downscaled and bias-corrected versions of NASA's Goddard Earth Observing System Model (GEOS-5), and NOAA's Climate Forecast System (CFSv2) forecasts

  19. Benchmarking hydrological models for low-flow simulation and forecasting on French catchments

    Science.gov (United States)

    Nicolle, P.; Pushpalatha, R.; Perrin, C.; François, D.; Thiéry, D.; Mathevet, T.; Le Lay, M.; Besson, F.; Soubeyroux, J.-M.; Viel, C.; Regimbeau, F.; Andréassian, V.; Maugis, P.; Augeard, B.; Morice, E.

    2014-08-01

    Low-flow simulation and forecasting remains a difficult issue for hydrological modellers, and intercomparisons can be extremely instructive for assessing existing low-flow prediction models and for developing more efficient operational tools. This research presents the results of a collaborative experiment conducted to compare low-flow simulation and forecasting models on 21 unregulated catchments in France. Five hydrological models (four lumped storage-type models - Gardenia, GR6J, Mordor and Presages - and one distributed physically oriented model - SIM) were applied within a common evaluation framework and assessed using a common set of criteria. Two simple benchmarks describing the average streamflow variability were used to set minimum levels of acceptability for model performance in simulation and forecasting modes. Results showed that, in simulation as well as in forecasting modes, all hydrological models performed almost systematically better than the benchmarks. Although no single model outperformed all the others for all catchments and criteria, a few models appeared to be more satisfactory than the others on average. In simulation mode, all attempts to relate model efficiency to catchment or streamflow characteristics remained inconclusive. In forecasting mode, we defined maximum useful forecasting lead times beyond which the model does not bring useful information compared to the benchmark. This maximum useful lead time logically varies between catchments, but also depends on the model used. Simple multi-model approaches that combine the outputs of the five hydrological models were tested to improve simulation and forecasting efficiency. We found that the multi-model approach was more robust and could provide better performance than individual models on average.

  20. Forecasting Volatility of Dhaka Stock Exchange: Linear Vs Non-linear models

    Directory of Open Access Journals (Sweden)

    Masudul Islam

    2012-10-01

    Full Text Available Prior information about a financial market is very essential for investor to invest money on parches share from the stock market which can strengthen the economy. The study examines the relative ability of various models to forecast daily stock indexes future volatility. The forecasting models that employed from simple to relatively complex ARCH-class models. It is found that among linear models of stock indexes volatility, the moving average model ranks first using root mean square error, mean absolute percent error, Theil-U and Linex loss function  criteria. We also examine five nonlinear models. These models are ARCH, GARCH, EGARCH, TGARCH and restricted GARCH models. We find that nonlinear models failed to dominate linear models utilizing different error measurement criteria and moving average model appears to be the best. Then we forecast the next two months future stock index price volatility by the best (moving average model.

  1. Using synchronization to improve the forecasting of large relaxations in a cellular-automaton model

    DEFF Research Database (Denmark)

    González, Á.; Gómez, J.B.; Vázquez-Prada, M.

    2004-01-01

    A new forecasting strategy for stochastic systems is introduced. It is inspired by the concept of synchronization, developed in the area of Dynamical Systems, and by the earthquake forecasting algorithms in which different pattern recognition functions are used for identifying seismic premonitory...... system that efficiently marks the impending occurrence of a catastrophic event. The power of this method is quantitatively illustrated by forecasting the occurrence of the largest relaxations in the so-called Minimalist Model.......A new forecasting strategy for stochastic systems is introduced. It is inspired by the concept of synchronization, developed in the area of Dynamical Systems, and by the earthquake forecasting algorithms in which different pattern recognition functions are used for identifying seismic premonitory...

  2. Modeling and forecasting of wind power generation - Regime-switching approaches

    DEFF Research Database (Denmark)

    Trombe, Pierre-Julien

    of more renewable energy into power systems since these systems are subjected to maintain a strict balance between electricity consumption and production, at any time. For this purpose, wind power forecasts offer an essential support to power system operators. In particular, there is a growing demand...... of high and low variability. They also yield substantial gains in probabilistic forecast accuracy for lead times of a few minutes. However, these models only integrate historical and local measurements of wind power and thus have a limited ability for notifying regime changes for larger lead times....... For that purpose, there is a long tradition in using meteorological forecasts of wind speed and direction that are converted into wind power forecasts. Nevertheless, meteorological forecasts are not informative on the intra-hour wind variability and thus cannot be used in the present context focusing on temporal...

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

    Directory of Open Access Journals (Sweden)

    Xiuli Zhao

    2014-01-01

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

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

    Science.gov (United States)

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

    2014-01-01

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

  5. Modeling Extra-Long Tsunami Propagation: Assessing Data, Model Accuracy and Forecast Implications

    Science.gov (United States)

    Titov, V. V.; Moore, C. W.; Rabinovich, A.

    2017-12-01

    Detecting and modeling tsunamis propagating tens of thousands of kilometers from the source is a formidable scientific challenge and seemingly satisfies only scientific curiosity. However, results of such analyses produce a valuable insight into the tsunami propagation dynamics, model accuracy and would provide important implications for tsunami forecast. The Mw = 9.3 megathrust earthquake of December 26, 2004 off the coast of Sumatra generated a tsunami that devastated Indian Ocean coastlines and spread into the Pacific and Atlantic oceans. The tsunami was recorded by a great number of coastal tide gauges, including those located in 15-25 thousand kilometers from the source area. To date, it is still the farthest instrumentally detected tsunami. The data from these instruments throughout the world oceans enabled to estimate various statistical parameters and energy decay of this event. High-resolution records of this tsunami from DARTs 32401 (offshore of northern Chile), 46405 and NeMO (both offshore of the US West Coast), combined with the mainland tide gauge measurements enabled us to examine far-field characteristics of the 2004 in the Pacific Ocean and to compare the results of global numerical simulations with the observations. Despite their small heights (less than 2 cm at deep-ocean locations), the records demonstrated consistent spatial and temporal structure. The numerical model described well the frequency content, amplitudes and general structure of the observed waves at deep-ocean and coastal gages. We present analysis of the measurements and comparison with model data to discuss implication for tsunami forecast accuracy. Model study for such extreme distances from the tsunami source and at extra-long times after the event is an attempt to find accuracy bounds for tsunami models and accuracy limitations of model use for forecast. We discuss results in application to tsunami model forecast and tsunami modeling in general.

  6. 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.

  7. Forecasting Euro Area Inflation Using Single-Equation and Multivariate VAR–Models

    Directory of Open Access Journals (Sweden)

    Gerdesmeier Dieter

    2017-12-01

    Full Text Available Forecasting inflation is of key relevance for central banks, not least because the objective of low and stable inflation is embodied in most central banks’ mandates and the monetary policy transmission mechanism is well known to be subject to long and variable lags. To our best knowledge, central banks around the world use conditional as well as unconditional forecasts for such purposes. Turning to unconditional forecasts, these can be derived on the basis of structural and non-structural models. Among the latter, vector autoregressive (VAR-models are among the most popular tools.

  8. Integrating uncertainty in time series population forecasts: An illustration using a simple projection model

    Directory of Open Access Journals (Sweden)

    Guy J. Abel

    2013-12-01

    Full Text Available Background: Population forecasts are widely used for public policy purposes. Methods to quantify the uncertainty in forecasts tend to ignore model uncertainty and to be based on a single model. Objective: In this paper, we use Bayesian time series models to obtain future population estimates with associated measures of uncertainty. The models are compared based on Bayesian posterior model probabilities, which are then used to provide model-averaged forecasts. Methods: The focus is on a simple projection model with the historical data representing population change in England and Wales from 1841 to 2007. Bayesian forecasts to the year 2032 are obtained based on a range of models, including autoregression models, stochastic volatility models and random variance shift models. The computational steps to fit each of these models using the OpenBUGS software via R are illustrated. Results: We show that the Bayesian approach is adept in capturing multiple sources of uncertainty in population projections, including model uncertainty. The inclusion of non-constant variance improves the fit of the models and provides more realistic predictive uncertainty levels. The forecasting methodology is assessed through fitting the models to various truncated data series.

  9. Fishery landing forecasting using EMD-based least square support vector machine models

    Science.gov (United States)

    Shabri, Ani

    2015-05-01

    In this paper, the novel hybrid ensemble learning paradigm integrating ensemble empirical mode decomposition (EMD) and least square support machine (LSSVM) is proposed to improve the accuracy of fishery landing forecasting. This hybrid is formulated specifically to address in modeling fishery landing, which has high nonlinear, non-stationary and seasonality time series which can hardly be properly modelled and accurately forecasted by traditional statistical models. In the hybrid model, EMD is used to decompose original data into a finite and often small number of sub-series. The each sub-series is modeled and forecasted by a LSSVM model. Finally the forecast of fishery landing is obtained by aggregating all forecasting results of sub-series. To assess the effectiveness and predictability of EMD-LSSVM, monthly fishery landing record data from East Johor of Peninsular Malaysia, have been used as a case study. The result shows that proposed model yield better forecasts than Autoregressive Integrated Moving Average (ARIMA), LSSVM and EMD-ARIMA models on several criteria..

  10. Hurricane Sandy, Disaster Preparedness, and the Recovery Model.

    Science.gov (United States)

    Pizzi, Michael A

    2015-01-01

    Hurricane Sandy was the second largest and costliest hurricane in U.S. history to affect multiple states and communities. This article describes the lived experiences of 24 occupational therapy students who lived through Hurricane Sandy using the Recovery Model to frame the research. Occupational therapy student narratives were collected and analyzed using qualitative methods and framed by the Recovery Model. Directed content and thematic analysis was performed using the 10 components of the Recovery Model. The 10 components of the Recovery Model were experienced by or had an impact on the occupational therapy students as they coped and recovered in the aftermath of the natural disaster. This study provides insight into the lived experiences and recovery perspectives of occupational therapy students who experienced Hurricane Sandy. Further research is indicated in applying the Recovery Model to people who survive disasters. Copyright © 2015 by the American Occupational Therapy Association, Inc.

  11. Four wind speed multi-step forecasting models using extreme learning machines and signal decomposing algorithms

    International Nuclear Information System (INIS)

    Liu, Hui; Tian, Hong-qi; Li, Yan-fei

    2015-01-01

    Highlights: • A hybrid architecture is proposed for the wind speed forecasting. • Four algorithms are used for the wind speed multi-scale decomposition. • The extreme learning machines are employed for the wind speed forecasting. • All the proposed hybrid models can generate the accurate results. - Abstract: Realization of accurate wind speed forecasting is important to guarantee the safety of wind power utilization. In this paper, a new hybrid forecasting architecture is proposed to realize the wind speed accurate forecasting. In this architecture, four different hybrid models are presented by combining four signal decomposing algorithms (e.g., Wavelet Decomposition/Wavelet Packet Decomposition/Empirical Mode Decomposition/Fast Ensemble Empirical Mode Decomposition) and Extreme Learning Machines. The originality of the study is to investigate the promoted percentages of the Extreme Learning Machines by those mainstream signal decomposing algorithms in the multiple step wind speed forecasting. The results of two forecasting experiments indicate that: (1) the method of Extreme Learning Machines is suitable for the wind speed forecasting; (2) by utilizing the decomposing algorithms, all the proposed hybrid algorithms have better performance than the single Extreme Learning Machines; (3) in the comparisons of the decomposing algorithms in the proposed hybrid architecture, the Fast Ensemble Empirical Mode Decomposition has the best performance in the three-step forecasting results while the Wavelet Packet Decomposition has the best performance in the one and two step forecasting results. At the same time, the Wavelet Packet Decomposition and the Fast Ensemble Empirical Mode Decomposition are better than the Wavelet Decomposition and the Empirical Mode Decomposition in all the step predictions, respectively; and (4) the proposed algorithms are effective in the wind speed accurate predictions

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

    DEFF Research Database (Denmark)

    Lunde, Asger; Olesen, Kasper Vinther

    measures of volatility. An influential bias in these measures is documented, which motivates the use of a flexible and robust methodology such as the Realized GARCH. Within this framework, forecasting of the full density for long horizons is feasible, which we pursue. We document variability in conditional...... 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...... frequency and method have significant implications for model fit in-sample. Finally, we consider an extensive out-of-sample exercise to forecast the conditional return distribution. The out-of-sample results for the Realized GARCH forecasts suggest a limited added value from using “traditional” realized...

  13. Analyzing the Assignment of Enlisted Recruiting Goal Shares via the Navy's Enlisted Goaling and Forecasting Model

    National Research Council Canada - National Science Library

    Hojnowski, Ronald A

    2005-01-01

    ...) and assigned to Navy Regional recruiting commands. Through use of an econometric goaling and forecasting model employed by CNRC and a less complicated weighting system used by Regions, goal shares are generated using factors believed to accurately...

  14. Forecasting hourly global solar radiation using hybrid k-means and nonlinear autoregressive neural network models

    International Nuclear Information System (INIS)

    Benmouiza, Khalil; Cheknane, Ali

    2013-01-01

    Highlights: • An unsupervised clustering algorithm with a neural network model was explored. • The forecasting results of solar radiation time series and the comparison of their performance was simulated. • A new method was proposed combining k-means algorithm and NAR network to provide better prediction results. - Abstract: In this paper, we review our work for forecasting hourly global horizontal solar radiation based on the combination of unsupervised k-means clustering algorithm and artificial neural networks (ANN). k-Means algorithm focused on extracting useful information from the data with the aim of modeling the time series behavior and find patterns of the input space by clustering the data. On the other hand, nonlinear autoregressive (NAR) neural networks are powerful computational models for modeling and forecasting nonlinear time series. Taking the advantage of both methods, a new method was proposed combining k-means algorithm and NAR network to provide better forecasting results

  15. Short-Term City Electric Load Forecasting with Considering Temperature Effects: An Improved ARIMAX Model

    Directory of Open Access Journals (Sweden)

    Herui Cui

    2015-01-01

    Full Text Available Short-term electric load is significantly affected by weather, especially the temperature effects in summer. External factors can result in mutation structures in load data. Under the influence of the external temperature factors, city electric load cannot be easily forecasted as usual. This research analyzes the relationship between electricity load and daily temperature in city. An improved ARIMAX model is proposed in this paper to deal with the mutation data structures. It is found that information amount of the improved ARIMAX model is smaller than that of the classic method and its relative error is less than AR, ARMA and Sigmoid-Function ANN models. The forecasting results are more accurately fitted. This improved model is highly valuable when dealing with mutation data structure in the field of load forecasting. And it is also an effective technique in forecasting electric load with temperature effects.

  16. Forecasting performances of three automated modelling techniques during the economic crisis 2007-2009

    DEFF Research Database (Denmark)

    Kock, Anders Bredahl; Teräsvirta, Timo

    2014-01-01

    In this work we consider the forecasting of macroeconomic variables during an economic crisis. The focus is on a specific class of models, the so-called single hidden-layer feed-forward autoregressive neural network models. What makes these models interesting in the present context is the fact...... Scandinavian ones, and focus on forecasting during the economic crisis 2007–2009. The forecast accuracy is measured using the root mean square forecast error. Hypothesis testing is also used to compare the performances of the different techniques....... that they form a class of universal approximators and may be expected to work well during exceptional periods such as major economic crises. Neural network models are often difficult to estimate, and we follow the idea of White (2006) of transforming the specification and nonlinear estimation problem...

  17. Development of a Multi-Model Ensemble Scheme for the Tropical Cyclone Forecast

    Science.gov (United States)

    Jun, S.; Lee, W. J.; Kang, K.; Shin, D. H.

    2015-12-01

    A Multi-Model Ensemble (MME) prediction scheme using selected and weighted method was developed and evaluated for tropical cyclone forecast. The analyzed tropical cyclone track and intensity data set provided by Korea Meteorological Administration and 11 numerical model outputs - GDAPS, GEPS, GFS (data resolution; 50 and 100 km), GFES, HWRF, IFS(data resolution; 50 and 100 km), IFS EPS, JGSM, and TEPS - during 2011-2014 were used for this study. The procedure suggested in this study was divided into two stages: selecting and weighting process. First several numerical models were chosen based on the past model's performances in the selecting stage. Next, weights, referred to as regression coefficients, for each model forecasts were calculated by applying the linear and nonlinear regression technique to past model forecast data in the weighting stage. Finally, tropical cyclone forecasts were determined by using both selected and weighted multi-model values at that forecast time. The preliminary result showed that selected MME's improvement rate (%) was more than 5% comparing with non-selected MME at 72 h track forecast.

  18. Hourly runoff forecasting for flood risk management: Application of various computational intelligence models

    Science.gov (United States)

    Badrzadeh, Honey; Sarukkalige, Ranjan; Jayawardena, A. W.

    2015-10-01

    Reliable river flow forecasts play a key role in flood risk mitigation. Among different approaches of river flow forecasting, data driven approaches have become increasingly popular in recent years due to their minimum information requirements and ability to simulate nonlinear and non-stationary characteristics of hydrological processes. In this study, attempts are made to apply four different types of data driven approaches, namely traditional artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), wavelet neural networks (WNN), and, hybrid ANFIS with multi resolution analysis using wavelets (WNF). Developed models applied for real time flood forecasting at Casino station on Richmond River, Australia which is highly prone to flooding. Hourly rainfall and runoff data were used to drive the models which have been used for forecasting with 1, 6, 12, 24, 36 and 48 h lead-time. The performance of models further improved by adding an upstream river flow data (Wiangaree station), as another effective input. All models perform satisfactorily up to 12 h lead-time. However, the hybrid wavelet-based models significantly outperforming the ANFIS and ANN models in the longer lead-time forecasting. The results confirm the robustness of the proposed structure of the hybrid models for real time runoff forecasting in the study area.

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

    International Nuclear Information System (INIS)

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

    2015-01-01

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

  20. Evaluation of model-based seasonal streamflow and water allocation forecasts for the Elqui Valley, Chile

    Science.gov (United States)

    Delorit, Justin; Cristian Gonzalez Ortuya, Edmundo; Block, Paul

    2017-09-01

    In many semi-arid regions, multisectoral demands often stress available water supplies. Such is the case in the Elqui River valley of northern Chile, which draws on a limited-capacity reservoir to allocate 25 000 water rights. Delayed infrastructure investment forces water managers to address demand-based allocation strategies, particularly in dry years, which are realized through reductions in the volume associated with each water right. Skillful season-ahead streamflow forecasts have the potential to inform managers with an indication of future conditions to guide reservoir allocations. This work evaluates season-ahead statistical prediction models of October-January (growing season) streamflow at multiple lead times associated with manager and user decision points, and links predictions with a reservoir allocation tool. Skillful results (streamflow forecasts outperform climatology) are produced for short lead times (1 September: ranked probability skill score (RPSS) of 0.31, categorical hit skill score of 61 %). At longer lead times, climatological skill exceeds forecast skill due to fewer observations of precipitation. However, coupling the 1 September statistical forecast model with a sea surface temperature phase and strength statistical model allows for equally skillful categorical streamflow forecasts to be produced for a 1 May lead, triggered for 60 % of years (1950-2015), suggesting forecasts need not be strictly deterministic to be useful for water rights holders. An early (1 May) categorical indication of expected conditions is reinforced with a deterministic forecast (1 September) as more observations of local variables become available. The reservoir allocation model is skillful at the 1 September lead (categorical hit skill score of 53 %); skill improves to 79 % when categorical allocation prediction certainty exceeds 80 %. This result implies that allocation efficiency may improve when forecasts are integrated into reservoir decision frameworks. The

  1. Development and Application of Econometric Models for Forecasting and Analysis of Monetary Policy Scenarios

    OpenAIRE

    Malugin, Vladimir; Demidenko , Mikhail; Kalechits, Dmitry; Miksjuk , Alexei; Tsukarev , Taras

    2009-01-01

    A system of econometric models designed for forecasting target monetary indicators as well as conducting monetary policy scenarios analysis is presented. The econometric models integrated in the system are represented in the error correction form and are interlinked by means of monetary policy instruments variables, common exogenous variables characterizing external shocks, and monetary policy target endogenous variables. Forecast accuracy estimates and monetary policy analysis results are pr...

  2. Forecasting turbulent modes with nonparametric diffusion models: Learning from noisy data

    Science.gov (United States)

    Berry, Tyrus; Harlim, John

    2016-04-01

    In this paper, we apply a recently developed nonparametric modeling approach, the "diffusion forecast", to predict the time-evolution of Fourier modes of turbulent dynamical systems. While the diffusion forecasting method assumes the availability of a noise-free training data set observing the full state space of the dynamics, in real applications we often have only partial observations which are corrupted by noise. To alleviate these practical issues, following the theory of embedology, the diffusion model is built using the delay-embedding coordinates of the data. We show that this delay embedding biases the geometry of the data in a way which extracts the most stable component of the dynamics and reduces the influence of independent additive observation noise. The resulting diffusion forecast model approximates the semigroup solutions of the generator of the underlying dynamics in the limit of large data and when the observation noise vanishes. As in any standard forecasting problem, the forecasting skill depends crucially on the accuracy of the initial conditions. We introduce a novel Bayesian method for filtering the discrete-time noisy observations which works with the diffusion forecast to determine the forecast initial densities. Numerically, we compare this nonparametric approach with standard stochastic parametric models on a wide-range of well-studied turbulent modes, including the Lorenz-96 model in weakly chaotic to fully turbulent regimes and the barotropic modes of a quasi-geostrophic model with baroclinic instabilities. We show that when the only available data is the low-dimensional set of noisy modes that are being modeled, the diffusion forecast is indeed competitive to the perfect model.

  3. An empirical investigation on the forecasting ability of mallows model averaging in a macro economic environment

    Science.gov (United States)

    Yin, Yip Chee; Hock-Eam, Lim

    2012-09-01

    This paper investigates the forecasting ability of Mallows Model Averaging (MMA) by conducting an empirical analysis of five Asia countries, Malaysia, Thailand, Philippines, Indonesia and China's GDP growth rate. Results reveal that MMA has no noticeable differences in predictive ability compared to the general autoregressive fractional integrated moving average model (ARFIMA) and its predictive ability is sensitive to the effect of financial crisis. MMA could be an alternative forecasting method for samples without recent outliers such as financial crisis.

  4. Dust modelling and forecasting in the Barcelona Supercomputing Center: Activities and developments

    Energy Technology Data Exchange (ETDEWEB)

    Perez, C; Baldasano, J M; Jimenez-Guerrero, P; Jorba, O; Haustein, K; Basart, S [Earth Sciences Department. Barcelona Supercomputing Center. Barcelona (Spain); Cuevas, E [Izanaa Atmospheric Research Center. Agencia Estatal de Meteorologia, Tenerife (Spain); Nickovic, S [Atmospheric Research and Environment Branch, World Meteorological Organization, Geneva (Switzerland)], E-mail: carlos.perez@bsc.es

    2009-03-01

    The Barcelona Supercomputing Center (BSC) is the National Supercomputer Facility in Spain, hosting MareNostrum, one of the most powerful Supercomputers in Europe. The Earth Sciences Department of BSC operates daily regional dust and air quality forecasts and conducts intensive modelling research for short-term operational prediction. This contribution summarizes the latest developments and current activities in the field of sand and dust storm modelling and forecasting.

  5. Adaptive modelling and forecasting of offshore wind power fluctuations with Markov-switching autoregressive models

    DEFF Research Database (Denmark)

    Pinson, Pierre; Madsen, Henrik

    optimized is based on penalized maximum-likelihood, with exponential forgetting of past observations. MSAR models are then employed for 1-step-ahead point forecasting of 10-minute resolution time-series of wind power at two large offshore wind farms. They are favourably compared against persistence and Auto......Wind power production data at temporal resolutions of a few minutes exhibits successive periods with fluctuations of various dynamic nature and magnitude, which cannot be explained (so far) by the evolution of some explanatory variable. Our proposal is to capture this regime-switching behaviour......Regressive (AR) models. It is finally shown that the main interest of MSAR models lies in their ability to generate interval/density forecasts of significantly higher skill....

  6. Adaptive modelling and forecasting of offshore wind power fluctuations with Markov-switching autoregressive models

    DEFF Research Database (Denmark)

    Pinson, Pierre; Madsen, Henrik

    2012-01-01

    optimized is based on penalized maximum likelihood, with exponential forgetting of past observations. MSAR models are then employed for one-step-ahead point forecasting of 10 min resolution time series of wind power at two large offshore wind farms. They are favourably compared against persistence......Wind power production data at temporal resolutions of a few minutes exhibit successive periods with fluctuations of various dynamic nature and magnitude, which cannot be explained (so far) by the evolution of some explanatory variable. Our proposal is to capture this regime-switching behaviour...... and autoregressive models. It is finally shown that the main interest of MSAR models lies in their ability to generate interval/density forecasts of significantly higher skill....

  7. Electricity Price Forecast Using Combined Models with Adaptive Weights Selected and Errors Calibrated by Hidden Markov Model

    Directory of Open Access Journals (Sweden)

    Da Liu

    2013-01-01

    Full Text Available A combined forecast with weights adaptively selected and errors calibrated by Hidden Markov model (HMM is proposed to model the day-ahead electricity price. Firstly several single models were built to forecast the electricity price separately. Then the validation errors from every individual model were transformed into two discrete sequences: an emission sequence and a state sequence to build the HMM, obtaining a transmission matrix and an emission matrix, representing the forecasting ability state of the individual models. The combining weights of the individual models were decided by the state transmission matrixes in HMM and the best predict sample ratio of each individual among all the models in the validation set. The individual forecasts were averaged to get the combining forecast with the weights obtained above. The residuals of combining forecast were calibrated by the possible error calculated by the emission matrix of HMM. A case study of day-ahead electricity market of Pennsylvania-New Jersey-Maryland (PJM, USA, suggests that the proposed method outperforms individual techniques of price forecasting, such as support vector machine (SVM, generalized regression neural networks (GRNN, day-ahead modeling, and self-organized map (SOM similar days modeling.

  8. Combination of Deterministic and Probabilistic Meteorological Models to enhance Wind Farm Power Forecasts

    International Nuclear Information System (INIS)

    Bremen, Lueder von

    2007-01-01

    Large-scale wind farms will play an important role in the future worldwide energy supply. However, with increasing wind power penetration all stakeholders on the electricity market will ask for more skilful wind power predictions regarding save grid integration and to increase the economic value of wind power. A Neural Network is used to calculate Model Output Statistics (MOS) for each individual forecast model (ECMWF and HIRLAM) and to model the aggregated power curve of the Middelgrunden offshore wind farm. We showed that the combination of two NWP models clearly outperforms the better single model. The normalized day-ahead RMSE forecast error for Middelgrunden can be reduced by 1% compared to single ECMWF. This is a relative improvement of 6%. For lead times >24h it is worthwhile to use a more sophisticated model combination approach than simple linear weighting. The investigated principle component regression is able to extract the uncorrelated information from two NWP forecasts. The spread of Ensemble Predictions is related to the skill of wind power forecasts. Simple contingency diagrams show that low spread corresponds is more often related to low forecast errors and high spread to large forecast errors

  9. Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models

    International Nuclear Information System (INIS)

    Nguyen, Hang T.; Nabney, Ian T.

    2010-01-01

    This paper presents some forecasting techniques for energy demand and price prediction, one day ahead. These techniques combine wavelet transform (WT) with fixed and adaptive machine learning/time series models (multi-layer perceptron (MLP), radial basis functions, linear regression, or GARCH). To create an adaptive model, we use an extended Kalman filter or particle filter to update the parameters continuously on the test set. The adaptive GARCH model is a new contribution, broadening the applicability of GARCH methods. We empirically compared two approaches of combining the WT with prediction models: multicomponent forecasts and direct forecasts. These techniques are applied to large sets of real data (both stationary and non-stationary) from the UK energy markets, so as to provide comparative results that are statistically stronger than those previously reported. The results showed that the forecasting accuracy is significantly improved by using the WT and adaptive models. The best models on the electricity demand/gas price forecast are the adaptive MLP/GARCH with the multicomponent forecast; their NMSEs are 0.02314 and 0.15384 respectively. (author)

  10. Approximation to a model for forecasting radiation fog in Rionegro-Antioquia

    International Nuclear Information System (INIS)

    Aguilar Gonzalez, Ana Isabel; Leon Aristizabal, Gloria

    2004-01-01

    Based on the discriminant analysis method, the modeling of the fog was carried out in order to forecast its occurrence with 3, 5 and 6 hours of anticipation, considering as the predictor variables: air temperature, dew point depression, strength of the surface wind, heat flow in the ground, latent heat flow, sensible heat flow, and the friction velocity. The values of these variables are taken from the hourly surface weather data at the airport in question, whereas the micro meteorological type variables are obtained through the program CAPLIM, thus enabling this model to be applied with operative aims. The forecast skill of the model depends on the prior time span of prediction. Three hours ahead of the occurrence of the phenomenon, the forecasts are right in 76% of cases, whereas if forecast five to six hours in advance the skill scores 53%

  11. Intuitionistic Fuzzy Time Series Forecasting Model Based on Intuitionistic Fuzzy Reasoning

    Directory of Open Access Journals (Sweden)

    Ya’nan Wang

    2016-01-01

    Full Text Available Fuzzy sets theory cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. In this regard, an intuitionistic fuzzy time series forecasting model is built. In the new model, a fuzzy clustering algorithm is used to divide the universe of discourse into unequal intervals, and a more objective technique for ascertaining the membership function and nonmembership function of the intuitionistic fuzzy set is proposed. On these bases, forecast rules based on intuitionistic fuzzy approximate reasoning are established. At last, contrast experiments on the enrollments of the University of Alabama and the Taiwan Stock Exchange Capitalization Weighted Stock Index are carried out. The results show that the new model has a clear advantage of improving the forecast accuracy.

  12. SIS epidemiological model for adaptive RT: Forecasting the parotid glands shrinkage during tomotherapy treatment.

    Science.gov (United States)

    Maffei, Nicola; Guidi, Gabriele; Vecchi, Claudio; Ciarmatori, Alberto; Gottardi, Giovanni; Meduri, Bruno; D'Angelo, Elisa; Bruni, Alessio; Mazzeo, Ercole; Pratissoli, Silvia; Giacobazzi, Patrizia; Baldazzi, Giuseppe; Lohr, Frank; Costi, Tiziana

    2016-07-01

    A susceptible-infected-susceptible (SIS) epidemic model was applied to radiation therapy (RT) treatments to predict morphological variations in head and neck (H&N) anatomy. 360 daily MVCT images of 12 H&N patients treated by tomotherapy were analyzed in this retrospective study. Deformable image registration (DIR) algorithms, mesh grids, and structure recontouring, implemented in the RayStation treatment planning system (TPS), were applied to assess the daily organ warping. The parotid's warping was evaluated using the epidemiological approach considering each vertex as a single subject and its deformed vector field (DVF) as an infection. Dedicated IronPython scripts were developed to export daily coordinates and displacements of the region of interest (ROI) from the TPS. matlab tools were implemented to simulate the SIS modeling. Finally, the fully trained model was applied to a new patient. A QUASAR phantom was used to validate the model. The patients' validation was obtained setting 0.4 cm of vertex displacement as threshold and splitting susceptible (S) and infectious (I) cases. The correlation between the epidemiological model and the parotids' trend for further optimization of alpha and beta was carried out by Euclidean and dynamic time warping (DTW) distances. The best fit with experimental conditions across all patients (Euclidean distance of 4.09 ± 1.12 and DTW distance of 2.39 ± 0.66) was obtained setting the contact rate at 7.55 ± 0.69 and the recovery rate at 2.45 ± 0.26; birth rate was disregarded in this constant population. Combining an epidemiological model with adaptive RT (ART), the authors' novel approach could support image-guided radiation therapy (IGRT) to validate daily setup and to forecast anatomical variations. The SIS-ART model developed could support clinical decisions in order to optimize timing of replanning achieving personalized treatments.

  13. Systematic and Integrated Approach to Tropical Cyclone Track Forecasting. Part 3. Traits Knowledge Base for JTWC Track Forecast Models in the Western North Pacific

    National Research Council Canada - National Science Library

    Carr, Lester

    1999-01-01

    .... The indications and characteristics of these frequently recurring error mechanisms in the forecast tracks and fields of the models are thoroughly documented and one or more illustrative case studies...

  14. A meteo-hydrological prediction system based on a multi-model approach for precipitation forecasting

    Directory of Open Access Journals (Sweden)

    S. Davolio

    2008-02-01

    Full Text Available The precipitation forecasted by a numerical weather prediction model, even at high resolution, suffers from errors which can be considerable at the scales of interest for hydrological purposes. In the present study, a fraction of the uncertainty related to meteorological prediction is taken into account by implementing a multi-model forecasting approach, aimed at providing multiple precipitation scenarios driving the same hydrological model. Therefore, the estimation of that uncertainty associated with the quantitative precipitation forecast (QPF, conveyed by the multi-model ensemble, can be exploited by the hydrological model, propagating the error into the hydrological forecast.

    The proposed meteo-hydrological forecasting system is implemented and tested in a real-time configuration for several episodes of intense precipitation affecting the Reno river basin, a medium-sized basin located in northern Italy (Apennines. These episodes are associated with flood events of different intensity and are representative of different meteorological configurations responsible for severe weather affecting northern Apennines.

    The simulation results show that the coupled system is promising in the prediction of discharge peaks (both in terms of amount and timing for warning purposes. The ensemble hydrological forecasts provide a range of possible flood scenarios that proved to be useful for the support of civil protection authorities in their decision.

  15. An integrated system for wind energy forecast using meteorological models and statistical post-processing

    Science.gov (United States)

    Miranda, P.; Rodrigues, A.; Lopes, J.; Palma, J.; Tome, R.; Sousa, J.; Bessa, R.; Matos, J.

    2009-12-01

    With 3GW of installed wind turbines, corresponding to 23% of the total electric grid, and a 5-year plan that will grow that value above 5GW (near 40% of the grid), Portugal has been a recent success case for renewable energy development. Clearly such large share of wind energy in the national electric system implies a strong requirement for accurate wind forecasts, that can be used to forecast this highly variable energy source and allow for timely decision making in the energy markets, namely for on and off switching of alternative conventional sources. In the past 3 years, a system for 72h energy forecast in mainland Portugal was setup, using 6km resolution meteorological forecasts, forced by global GFS forecasts by NCEP. In the development phase, different boundary conditions (from NCEP and ECMWF) were tested, as well as different limited area models (namely MM5, Aladin, MesoNH and WRF) at resolutions from 12 to 2km, which were evaluated by comparison with wind observations at heights relevant for wind turbines (up to 80m) in different locations and for different synoptic conditions. The developed system, which works with a real time connection with wind farms, also includes a post-processing code that merges recent wind observations with the meteorological forecast, and converts the forecasted wind fields into forecasted energy, by incorporating empirical transfer functions of the wind farm. Wind conditions in Portugal are highly influenced by topography, as most wind farms are located in complex terrain, often in mountainous terrain, where stratification plays a significant role. Coastal effects are also highly relevant, especially during the Summer, where a strong diurnal cycle of the sea-breeze is superimposed on an equally strong boundary layer development, both with a significant impact on low level winds. These two ingredients tend to complicate wind forecasts, requiring fully developed meteorological models. In general, results from 2 full years of

  16. Streamflow forecast in the Alto do Rio Doce watershed in Brazil, using hydrological and atmospheric model

    Science.gov (United States)

    Silva, J. M.; Saad, S. I.; Palma, G.; Rocha, H.; Palmeira, R. M.; Silva, B. L.; Pessoa, A. A.; Ramos, C. G.; Cecchini, M. A.

    2013-05-01

    Electrical energy in Brazil depends essentially on the streamflow, as hydropowers accounts for up to 79% of the total electrical energy installed capacity. Therefore, streamflow forecasts are very important tools to assist in the planning and operation of Brazilian hydroelectric reservoirs. This study evaluated the performance of a distributed hydrological model, Soil and Water Assessment Tool (SWAT) daily streamflow forecasts into four Reservoirs sited in the Alto do Rio Doce Watershed, in Southeast of Brazil. SWAT model was used with precipitation forecast from the regional meteorological model MM5. The calibration and validation processes of SWAT were accomplished using data from four monitoring stations. The model has been run for the 2010-2012 period, and while the apr/2010-set/2011 period has been used for calibration conducted manually, the validation reached the rest of the period. The manual calibration was conducted by the means of sensibility tests of parameters that control surface runoff and groundwater flow, specially the surlag and alpha_bf, respectively the surface runoff lag coefficient and the baseflow recession constant. The daily and monthly Nash-Sutcliffe, R2 and the mean relative error performance indicators were used to assess the relative performance of the model. Results showed that streamflow forecast was very similar toobservations, except in reservoirs with lower drainage areas, where the model did not simulated the beginning of the flood (Dec-Feb). The streamflow forecasts was strongly dependent on the quality of precipitation forecasts used. Given that no correction in the simulated rainfall by the MM5 model in the Alto do Rio Doce watershed has been conducted and no automated calibration method was applied to the parameters of the hydrologic model, we can conclude that the application of the SWAT hydrologic model employing the output data from the MM5 atmospheric model for the streamflow forecast was shown to be a tool of great

  17. Forecasting the weather at the TAL sites during STS-40 using the grid point forecast output from the NMC MRF model

    Science.gov (United States)

    Hafele, Gene M.

    1992-01-01

    The NOAA's Spaceflight Meteorology Group has used the point forecast output from the Global Profile Archive and Global Profile Archive since 1990, and found this product to allow forecasters to examine the MRF model in a vertical profile, and thereby determine how different model parameters behave over time. Attention is presently given to the use of these resources in the illustrative case of the STS-40 mission, over northwestern Spain.

  18. Data Assimilation within the Advanced Circulation (ADCIRC) Modeling Framework for Hurricane Storm Surge Forecasting

    KAUST Repository

    Butler, T.

    2012-07-01

    Accurate, real-time forecasting of coastal inundation due to hurricanes and tropical storms is a challenging computational problem requiring high-fidelity forward models of currents and water levels driven by hurricane-force winds. Despite best efforts in computational modeling there will always be uncertainty in storm surge forecasts. In recent years, there has been significant instrumentation located along the coastal United States for the purpose of collecting data—specifically wind, water levels, and wave heights—during these extreme events. This type of data, if available in real time, could be used in a data assimilation framework to improve hurricane storm surge forecasts. In this paper a data assimilation methodology for storm surge forecasting based on the use of ensemble Kalman filters and the advanced circulation (ADCIRC) storm surge model is described. The singular evolutive interpolated Kalman (SEIK) filter has been shown to be effective at producing accurate results for ocean models using small ensemble sizes initialized by an empirical orthogonal function analysis. The SEIK filter is applied to the ADCIRC model to improve storm surge forecasting, particularly in capturing maximum water levels (high water marks) and the timing of the surge. Two test cases of data obtained from hindcast studies of Hurricanes Ike and Katrina are presented. It is shown that a modified SEIK filter with an inflation factor improves the accuracy of coarse-resolution forecasts of storm surge resulting from hurricanes. Furthermore, the SEIK filter requires only modest computational resources to obtain more accurate forecasts of storm surge in a constrained time window where forecasters must interact with emergency responders.

  19. 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

  20. Selecting the Best Forecasting-Implied Volatility Model Using Genetic Programming

    Directory of Open Access Journals (Sweden)

    Wafa Abdelmalek

    2009-01-01

    Full Text Available The volatility is a crucial variable in option pricing and hedging strategies. The aim of this paper is to provide some initial evidence of the empirical relevance of genetic programming to volatility's forecasting. By using real data from S&P500 index options, the genetic programming's ability to forecast Black and Scholes-implied volatility is compared between time series samples and moneyness-time to maturity classes. Total and out-of-sample mean squared errors are used as forecasting's performance measures. Comparisons reveal that the time series model seems to be more accurate in forecasting-implied volatility than moneyness time to maturity models. Overall, results are strongly encouraging and suggest that the genetic programming approach works well in solving financial problems.

  1. High resolution forecasting for wind energy applications using Bayesian model averaging

    Energy Technology Data Exchange (ETDEWEB)

    Courtney, Jennifer F.; Lynch, Peter; Sweeney, Conor [Meteorology and Climate Centre, UCD, Dublin (Ireland)], e-mail: jennifer.courtney@ucdconnect.ie

    2013-02-15

    Two methods of post-processing the uncalibrated wind speed forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (EPS) are presented here. Both methods involve statistically post-processing the EPS or a downscaled version of it with Bayesian model averaging (BMA). The first method applies BMA directly to the EPS data. The second method involves clustering the EPS to eight representative members (RMs) and downscaling the data through two limited area models at two resolutions. Four weighted ensemble mean forecasts are produced and used as input to the BMA method. Both methods are tested against 13 meteorological stations around Ireland with 1 yr of forecast/observation data. Results show calibration and accuracy improvements using both methods, with the best results stemming from Method 2, which has comparatively low mean absolute error and continuous ranked probability scores.

  2. High resolution forecasting for wind energy applications using Bayesian model averaging

    Directory of Open Access Journals (Sweden)

    Jennifer F. Courtney

    2013-02-01

    Full Text Available Two methods of post-processing the uncalibrated wind speed forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF ensemble prediction system (EPS are presented here. Both methods involve statistically post-processing the EPS or a downscaled version of it with Bayesian model averaging (BMA. The first method applies BMA directly to the EPS data. The second method involves clustering the EPS to eight representative members (RMs and downscaling the data through two limited area models at two resolutions. Four weighted ensemble mean forecasts are produced and used as input to the BMA method. Both methods are tested against 13 meteorological stations around Ireland with 1 yr of forecast/observation data. Results show calibration and accuracy improvements using both methods, with the best results stemming from Method 2, which has comparatively low mean absolute error and continuous ranked probability scores.

  3. Short-range forecasts with the GISS model of the global atmosphere

    Science.gov (United States)

    Druyan, L. M.

    1974-01-01

    A nine-layer, primitive equation (PE) model of the global atmosphere developed at the Goddard Institute for Space Studies (GISS) has been used to generate six 48-hr forecasts during December 1972 and January 1973. Operational analyses north of 18 N and experimental global analyses made available by the National Meteorological Center (NMC), NOAA, were used as the initial conditions; the operational analyses were used to verify the forecasts at 12-hr intervals over the Northern Hemisphere north of 22 N. The combined analyses were used to verify the forecasts in the global domain. RMS errors of the sea-level pressure, 1000-mb heights, and vector geostrophic winds, and 500-mb heights and vector geostrophic winds indicate that the GISS forecasts have skill comparable to those made by operational PE models.

  4. A physics-based probabilistic forecasting model for rainfall-induced shallow landslides at regional scale

    Science.gov (United States)

    Zhang, Shaojie; Zhao, Luqiang; Delgado-Tellez, Ricardo; Bao, Hongjun

    2018-03-01

    Conventional outputs of physics-based landslide forecasting models are presented as deterministic warnings by calculating the safety factor (Fs) of potentially dangerous slopes. However, these models are highly dependent on variables such as cohesion force and internal friction angle which are affected by a high degree of uncertainty especially at a regional scale, resulting in unacceptable uncertainties of Fs. Under such circumstances, the outputs of physical models are more suitable if presented in the form of landslide probability values. In order to develop such models, a method to link the uncertainty of soil parameter values with landslide probability is devised. This paper proposes the use of Monte Carlo methods to quantitatively express uncertainty by assigning random values to physical variables inside a defined interval. The inequality Fs forecasting model for rainfall-induced shallow landslides. The prediction ability of this model was tested in a case study, in which simulated forecasting of landslide disasters associated with heavy rainfalls on 9 July 2013 in the Wenchuan earthquake region of Sichuan province, China, was performed. The proposed model successfully forecasted landslides in 159 of the 176 disaster points registered by the geo-environmental monitoring station of Sichuan province. Such testing results indicate that the new model can be operated in a highly efficient way and show more reliable results, attributable to its high prediction accuracy. Accordingly, the new model can be potentially packaged into a forecasting system for shallow landslides providing technological support for the mitigation of these disasters at regional scale.

  5. Modeling and forecasting crude oil markets using ARCH-type models

    International Nuclear Information System (INIS)

    Cheong, Chin Wen

    2009-01-01

    This study investigates the time-varying volatility of two major crude oil markets, the West Texas Intermediate (WTI) and Europe Brent. A flexible autoregressive conditional heteroskedasticity (ARCH) model is used to take into account the stylized volatility facts such as clustering volatility, asymmetric news impact and long memory volatility among others. The empirical results indicate that the intensity of long-persistence volatility in the WTI is greater than in the Brent. It is also found that for the WTI, the appreciation and depreciation shocks of the WTI have similar impact on the resulting volatility. However, a leverage effect is found in Brent. Although both the estimation and diagnostic evaluations are in favor of an asymmetric long memory ARCH model, only the WTI models provide superior in the out-of-sample forecasts. On the other hand, from the empirical out-of-sample forecasts, it appears that the simplest parsimonious generalized ARCH provides the best forecasted evaluations for the Brent crude oil data. (author)

  6. Comparative Validation of Realtime Solar Wind Forecasting Using the UCSD Heliospheric Tomography Model

    Science.gov (United States)

    MacNeice, Peter; Taktakishvili, Alexandra; Jackson, Bernard; Clover, John; Bisi, Mario; Odstrcil, Dusan

    2011-01-01

    The University of California, San Diego 3D Heliospheric Tomography Model reconstructs the evolution of heliospheric structures, and can make forecasts of solar wind density and velocity up to 72 hours in the future. The latest model version, installed and running in realtime at the Community Coordinated Modeling Center(CCMC), analyzes scintillations of meter wavelength radio point sources recorded by the Solar-Terrestrial Environment Laboratory(STELab) together with realtime measurements of solar wind speed and density recorded by the Advanced Composition Explorer(ACE) Solar Wind Electron Proton Alpha Monitor(SWEPAM).The solution is reconstructed using tomographic techniques and a simple kinematic wind model. Since installation, the CCMC has been recording the model forecasts and comparing them with ACE measurements, and with forecasts made using other heliospheric models hosted by the CCMC. We report the preliminary results of this validation work and comparison with alternative models.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    1978-04-01

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

  8. 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.

  9. Long-term fashion forecast based on the sociological model of cyclic changes

    Directory of Open Access Journals (Sweden)

    А V Lebsak-Kleimans

    2010-09-01

    Full Text Available The concepts of social changes coined by classical sociology may be incorporated as the basis for the elaboration of social prognostication models which, in turn, may suitable for fashion forecast applied technologies development. In the framework of the given paper fashion is described as the phenomenon of collective behaviour. The principles of long-term fashion trends forecast are shown to be in line with the concepts of cyclic development.

  10. Forecasting Model for IPTV Service in Korea Using Bootstrap Ridge Regression Analysis

    Science.gov (United States)

    Lee, Byoung Chul; Kee, Seho; Kim, Jae Bum; Kim, Yun Bae

    The telecom firms in Korea are taking new step to prepare for the next generation of convergence services, IPTV. In this paper we described our analysis on the effective method for demand forecasting about IPTV broadcasting. We have tried according to 3 types of scenarios based on some aspects of IPTV potential market and made a comparison among the results. The forecasting method used in this paper is the multi generation substitution model with bootstrap ridge regression analysis.

  11. Forcing the snow-cover model SNOWPACK with forecasted weather data

    Directory of Open Access Journals (Sweden)

    S. Bellaire

    2011-12-01

    Full Text Available Avalanche danger is often estimated based on snow cover stratigraphy and snow stability data. In Canada, single forecasting regions are very large (>50 000 km2 and snow cover data are often not available. To provide additional information on the snow cover and its seasonal evolution the Swiss snow cover model SNOWPACK was therefore coupled with a regional weather forecasting model GEM15. The output of GEM15 was compared to meteorological as well as snow cover data from Mt. Fidelity, British Columbia, Canada, for five winters between 2005 and 2010. Precipitation amounts are most difficult to predict for weather forecasting models. Therefore, we first assess the capability of the model chain to forecast new snow amounts and consequently snow depth. Forecasted precipitation amounts were generally over-estimated. The forecasted data were therefore filtered and used as input for the snow cover model. Comparison between the model output and manual observations showed that after pre-processing the input data the snow depth and new snow events were well modelled. In a case study two key factors of snow cover instability, i.e. surface hoar formation and crust formation were investigated at a single point. Over half of the relevant critical layers were reproduced. Overall, the model chain shows promising potential as a future forecasting tool for avalanche warning services in Canadian data sparse areas and could thus well be applied to similarly large regions elsewhere. However, a more detailed analysis of the simulated snow cover structure is still required.

  12. A comparative verification of high resolution precipitation forecasts using model output statistics

    Science.gov (United States)

    van der Plas, Emiel; Schmeits, Maurice; Hooijman, Nicolien; Kok, Kees

    2017-04-01

    Verification of localized events such as precipitation has become even more challenging with the advent of high-resolution meso-scale numerical weather prediction (NWP). The realism of a forecast suggests that it should compare well against precipitation radar imagery with similar resolution, both spatially and temporally. Spatial verification methods solve some of the representativity issues that point verification gives rise to. In this study a verification strategy based on model output statistics is applied that aims to address both double penalty and resolution effects that are inherent to comparisons of NWP models with different resolutions. Using predictors based on spatial precipitation patterns around a set of stations, an extended logistic regression (ELR) equation is deduced, leading to a probability forecast distribution of precipitation for each NWP model, analysis and lead time. The ELR equations are derived for predictands based on areal calibrated radar precipitation and SYNOP observations. The aim is to extract maximum information from a series of precipitation forecasts, like a trained forecaster would. The method is applied to the non-hydrostatic model Harmonie (2.5 km resolution), Hirlam (11 km resolution) and the ECMWF model (16 km resolution), overall yielding similar Brier skill scores for the 3 post-processed models, but larger differences for individual lead times. Besides, the Fractions Skill Score is computed using the 3 deterministic forecasts, showing somewhat better skill for the Harmonie model. In other words, despite the realism of Harmonie precipitation forecasts, they only perform similarly or somewhat better than precipitation forecasts from the 2 lower resolution models, at least in the Netherlands.

  13. 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...... 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...

  14. Utilization of mesoscale atmospheric dynamic model PHYSIC as a meteorological forecast model in nuclear emergency response system

    International Nuclear Information System (INIS)

    Nagai, Haruyasu; Yamazawa, Hiromi

    1997-01-01

    It is advantageous for an emergency response system to have a forecast function to provide a time margin for countermeasures in case of a nuclear accident. We propose to apply an atmospheric dynamic model PHYSIC (Prognostic HYdroStatic model Including turbulence Closure model) as a meteorological forecast model in the emergency system. The model uses GPV data which are the output of the numerical weather forecast model of Japan Meteorological Agency as the initial and boundary conditions. The roles of PHYSIC are the interface between GPV data and the emergency response system and the forecast of local atmospheric phenomena within the model domain. This paper presents a scheme to use PHYSIC to forecast local wind and its performance. Horizontal grid number of PHYSIC is fixed to 50 x 50, whereas the mesh and domain sizes are determined in consideration of topography causing local winds at an objective area. The model performance was examined for the introduction of GPV data through initial and boundary conditions and the predictability of local wind field and atmospheric stability. The model performance was on an acceptable level as the forecast model. It was also recognized that improvement of cloud calculation was necessary in simulating atmospheric stability. (author)

  15. Statistical model for forecasting uranium prices to estimate the nuclear fuel cycle cost

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Sung Ki; Ko, Won Il; Nam, Hyoon [Nuclear Fuel Cycle Analysis, Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Kim, Chul Min; Chung, Yang Hon; Bang, Sung Sig [Korea Advanced Institute of Science and Technology, Daejeon (Korea, Republic of)

    2017-08-15

    This paper presents a method for forecasting future uranium prices that is used as input data to calculate the uranium cost, which is a rational key cost driver of the nuclear fuel cycle cost. In other words, the statistical autoregressive integrated moving average (ARIMA) model and existing engineering cost estimation method, the so-called escalation rate model, were subjected to a comparative analysis. When the uranium price was forecasted in 2015, the margin of error of the ARIMA model forecasting was calculated and found to be 5.4%, whereas the escalation rate model was found to have a margin of error of 7.32%. Thus, it was verified that the ARIMA model is more suitable than the escalation rate model at decreasing uncertainty in nuclear fuel cycle cost calculation.

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

    Directory of Open Access Journals (Sweden)

    Syed Aziz Ur Rehman

    2017-11-01

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

  17. LMDzT-INCA dust forecast model developments and associated validation efforts

    International Nuclear Information System (INIS)

    Schulz, M; Cozic, A; Szopa, S

    2009-01-01

    The nudged atmosphere global climate model LMDzT-INCA is used to forecast global dust fields. Evaluation is undertaken in retrospective for the forecast results of the year 2006. For this purpose AERONET/Photons sites in Northern Africa and on the Arabian Peninsula are chosen where aerosol optical depth is dominated by dust. Despite its coarse resolution, the model captures 48% of the day to day dust variability near Dakar on the initial day of the forecast. On weekly and monthly scale the model captures respectively 62% and 68% of the variability. Correlation coefficients between daily AOD values observed and modelled at Dakar decrease from 0.69 for the initial forecast day to 0.59 and 0.41 respectively for two days ahead and five days ahead. If one requests that the model should be able to issue a warning for an exceedance of aerosol optical depth of 0.5 and issue no warning in the other cases, then the model was wrong in 29% of the cases for day 0, 32% for day 2 and 35% for day 5. A reanalysis run with archived ECMWF winds is only slightly better (r=0.71) but was in error in 25% of the cases. Both the improved simulation of the monthly versus daily variability and the deterioration of the forecast with time can be explained by model failure to simulate the exact timing of a dust event.

  18. A Four-Stage Hybrid Model for Hydrological Time Series Forecasting

    Science.gov (United States)

    Di, Chongli; Yang, Xiaohua; Wang, Xiaochao

    2014-01-01

    Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of ‘denoising, decomposition and ensemble’. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models. PMID:25111782

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

    International Nuclear Information System (INIS)

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

    2012-01-01

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

  20. 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.

  1. Probabilistic Quantitative Precipitation Forecasting over East China using Bayesian Model Averaging

    Science.gov (United States)

    Yang, Ai; Yuan, Huiling

    2014-05-01

    The Bayesian model averaging (BMA) is a post-processing method that weights the predictive probability density functions (PDFs) of individual ensemble members. This study investigates the BMA method for calibrating quantitative precipitation forecasts (QPFs) from The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) database. The QPFs over East Asia during summer (June-August) 2008-2011 are generated from six operational ensemble prediction systems (EPSs), including ECMWF, UKMO, NCEP, CMC, JMA, CMA, and multi-center ensembles of their combinations. The satellite-based precipitation estimate product TRMM 3B42 V7 is used as the verification dataset. In the BMA post-processing for precipitation forecasts, the PDF matching method is first applied to bias-correct systematic errors in each forecast member, by adjusting PDFs of forecasts to match PDFs of observations. Next, a logistic regression and two-parameter gamma distribution are used to fit the probability of rainfall occurrence and precipitation distribution. Through these two steps, the BMA post-processing bias-corrects ensemble forecasts systematically. The 60-70% cumulative density function (CDF) predictions well estimate moderate precipitation compared to raw ensemble mean, while the 90% upper boundary of BMA CDF predictions can be set as a threshold of extreme precipitation alarm. In general, the BMA method is more capable of multi-center ensemble post-processing, which improves probabilistic QPFs (PQPFs) with better ensemble spread and reliability. KEYWORDS: Bayesian model averaging (BMA); post-processing; ensemble forecast; TIGGE

  2. FINANCIAL FUTURE PROSPECT INVESTIGATION USING BANKRUPTCY FORECASTING MODELS IN HUNGARIAN MEAT PROCESSING INDUSTRY

    Directory of Open Access Journals (Sweden)

    Dalma Peto

    2015-07-01

    Full Text Available Our main research topic is the analysis of leading companies in the Hungarian meat processing industry in terms of liquidity criteria. We examine this scientific subject by application of financial indicators and several important bankruptcy forecasting models. In our thesis the emphasis is placed on the presentation and evaluation of business failure models. The topicality of the research subject is rooted in the economic crisis and recession, which made solvency a key issue. Maintaining the competitive position in the market and the ability to stay in competition depend on the capability to generate an appropriate level of net operative cash flow. The most important research questions are the following. Which financial methods can be used to predict and estimate the situation when a company is facing bankruptcy? Do bankruptcy forecasting models provide accurate forecasts and what conclusions can be drawn based on these results? In our study we present the actual economic situation and the main problems of the sector, select the sample companies, calculate and compare the applied financial ratios and the most relevant bankruptcy forecasting models. On the basis of annual reports concerning 2010-2013 interval we investigate the financial position of leading pork processing companies. We make a comprehensive and comparative analysis concerning capital structure, liquidity, and profitability; consequently identify risky processes and companies having high probability of insolvency. Finally, we demonstrate and evaluate the results of three traditional bankruptcy forecasting models (Altman, Springate, and Fulmer and four modern models (DA, LR, industrial DA and industrial LR.

  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. Evaluation of a conceptual rainfall forecasting model from observed and simulated rain events

    Directory of Open Access Journals (Sweden)

    L. Dolciné

    1998-01-01

    Full Text Available Very short-term rainfall forecasting models designed for runoff analysis of catchments, particularly those subject to flash-floods, typically include one or more variables deduced from weather radars. Useful variables for defining the state and evolution of a rain system include rainfall rate, vertically integrated rainwater content and advection velocity. The forecast model proposed in this work complements recent dynamical formulations by focusing on a formulation incorporating these variables using volumetric radar data to define the model state variables, determining the rainfall source term directly from multi-scan radar data, explicitly accounting for orographic enhancement, and explicitly incorporating the dynamical model components in an advection-diffusion scheme. An evaluation of this model is presented for four rain events collected in the South of France and in the North-East of Italy. Model forecasts are compared with two simple methods: persistence and extrapolation. An additional analysis is performed using an existing mono-dimensional microphysical meteorological model to produce simulated rain events and provide initialization data. Forecasted rainfall produced by the proposed model and the extrapolation method are compared to the simulated events. The results show that the forecast model performance is influenced by rainfall temporal variability and performance is better for less variable rain events. The comparison with the extrapolation method shows that the proposed model performs better than extrapolation in the initial period of the forecast lead-time. It is shown that the performance of the proposed model over the extrapolation method depends essentially on the additional vertical information available from voluminal radar.

  5. Forecasting performance of three automated modelling techniques during the economic crisis 2007-2009

    DEFF Research Database (Denmark)

    Kock, Anders Bredahl; Teräsvirta, Timo

    In this work we consider forecasting macroeconomic variables during an economic crisis. The focus is on a speci…c class of models, the so-called single hidden-layer feedforward autoregressive neural network models. What makes these models interesting in the present context is that they form a class...... during the economic crisis 2007–2009. Forecast accuracy is measured by the root mean square forecast error. Hypothesis testing is also used to compare the performance of the different techniques with each other....... of universal approximators and may be expected to work well during exceptional periods such as major economic crises. These models are often difficult to estimate, and we follow the idea of White (2006) to transform the speci…cation and nonlinear estimation problem into a linear model selection and estimation...

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

    International Nuclear Information System (INIS)

    Mariner-Volpe, B.; Trapmann, W.

    1989-01-01

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

  7. An Experimental Protocol to Model Recovery of Anaerobic Work Capacity

    Directory of Open Access Journals (Sweden)

    Vijay Sarthy M. Sreedhara

    2018-02-01

    Full Text Available Models of fatigue are based on physiological parameters such as Critical Power (CP and Anaerobic Work Capacity (AWC. CP is a theoretical threshold value that a human can generate for an indefinite amount of time and AWC represents a finite expendable amount of anaerobic energy at intensities above CP. There is an increasing interest in developing mathematical models of energy expenditure and recovery for athletic training and human performance. The objective of this research is to propose and validate a model for recovery of AWC during a post exertion recovery interval of cycling. A cycling ergometer study is proposed which involves a VO2max ramp test to determine gas exchange threshold, a 3-min all-out intensity test to determine CP and AWC, and exertion-recovery interval tests to understand recovery of AWC. The results will be used to build a human in the loop control system to optimize cycling performance.

  8. Estimation and Forecasting in Vector Autoregressive Moving Average Models for Rich Datasets

    DEFF Research Database (Denmark)

    Dias, Gustavo Fruet; Kapetanios, George

    We address the issue of modelling and forecasting macroeconomic variables using rich datasets, by adopting the class of Vector Autoregressive Moving Average (VARMA) models. We overcome the estimation issue that arises with this class of models by implementing an iterative ordinary least squares (...

  9. WALS estimation and forecasting in factor-based dynamic models with an application to Armenia

    NARCIS (Netherlands)

    Poghosyan, K.; Magnus, J.R.

    2012-01-01

    Two model averaging approaches are used and compared in estimating and forecasting dynamic factor models, the well-known Bayesian model averaging (BMA) and the recently developed weighted average least squares (WALS). Both methods propose to combine frequentist estimators using Bayesian weights. We

  10. Comparison of point forecast accuracy of model averaging methods in hydrologic applications

    NARCIS (Netherlands)

    Diks, C.G.H.; Vrugt, J.A.

    2010-01-01

    Multi-model averaging is currently receiving a surge of attention in the atmospheric, hydrologic, and statistical literature to explicitly handle conceptual model uncertainty in the analysis of environmental systems and derive predictive distributions of model output. Such density forecasts are

  11. Diffusion Forecasting Model with Basis Functions from QR-Decomposition

    Science.gov (United States)

    Harlim, John; Yang, Haizhao

    2017-12-01

    The diffusion forecasting is a nonparametric approach that provably solves the Fokker-Planck PDE corresponding to Itô diffusion without knowing the underlying equation. The key idea of this method is to approximate the solution of the Fokker-Planck equation with a discrete representation of the shift (Koopman) operator on a set of basis functions generated via the diffusion maps algorithm. While the choice of these basis functions is provably optimal under appropriate conditions, computing these basis functions is quite expensive since it requires the eigendecomposition of an N× N diffusion matrix, where N denotes the data size and could be very large. For large-scale forecasting problems, only a few leading eigenvectors are computationally achievable. To overcome this computational bottleneck, a new set of basis functions constructed by orthonormalizing selected columns of the diffusion matrix and its leading eigenvectors is proposed. This computation can be carried out efficiently via the unpivoted Householder QR factorization. The efficiency and effectiveness of the proposed algorithm will be shown in both deterministically chaotic and stochastic dynamical systems; in the former case, the superiority of the proposed basis functions over purely eigenvectors is significant, while in the latter case forecasting accuracy is improved relative to using a purely small number of eigenvectors. Supporting arguments will be provided on three- and six-dimensional chaotic ODEs, a three-dimensional SDE that mimics turbulent systems, and also on the two spatial modes associated with the boreal winter Madden-Julian Oscillation obtained from applying the Nonlinear Laplacian Spectral Analysis on the measured Outgoing Longwave Radiation.

  12. Financial impact of errors in business forecasting: a comparative study of linear models and neural networks

    Directory of Open Access Journals (Sweden)

    Claudimar Pereira da Veiga

    2012-08-01

    Full Text Available The importance of demand forecasting as a management tool is a well documented issue. However, it is difficult to measure costs generated by forecasting errors and to find a model that assimilate the detailed operation of each company adequately. In general, when linear models fail in the forecasting process, more complex nonlinear models are considered. Although some studies comparing traditional models and neural networks have been conducted in the literature, the conclusions are usually contradictory. In this sense, the objective was to compare the accuracy of linear methods and neural networks with the current method used by the company. The results of this analysis also served as input to evaluate influence of errors in demand forecasting on the financial performance of the company. The study was based on historical data from five groups of food products, from 2004 to 2008. In general, one can affirm that all models tested presented good results (much better than the current forecasting method used, with mean absolute percent error (MAPE around 10%. The total financial impact for the company was 6,05% on annual sales.

  13. A novel grey-fuzzy-Markov and pattern recognition model for industrial accident forecasting

    Science.gov (United States)

    Edem, Inyeneobong Ekoi; Oke, Sunday Ayoola; Adebiyi, Kazeem Adekunle

    2017-10-01

    Industrial forecasting is a top-echelon research domain, which has over the past several years experienced highly provocative research discussions. The scope of this research domain continues to expand due to the continuous knowledge ignition motivated by scholars in the area. So, more intelligent and intellectual contributions on current research issues in the accident domain will potentially spark more lively academic, value-added discussions that will be of practical significance to members of the safety community. In this communication, a new grey-fuzzy-Markov time series model, developed from nondifferential grey interval analytical framework has been presented for the first time. This instrument forecasts future accident occurrences under time-invariance assumption. The actual contribution made in the article is to recognise accident occurrence patterns and decompose them into grey state principal pattern components. The architectural framework of the developed grey-fuzzy-Markov pattern recognition (GFMAPR) model has four stages: fuzzification, smoothening, defuzzification and whitenisation. The results of application of the developed novel model signify that forecasting could be effectively carried out under uncertain conditions and hence, positions the model as a distinctly superior tool for accident forecasting investigations. The novelty of the work lies in the capability of the model in making highly accurate predictions and forecasts based on the availability of small or incomplete accident data.

  14. Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based model

    Science.gov (United States)

    Pathak, Jaideep; Wikner, Alexander; Fussell, Rebeckah; Chandra, Sarthak; Hunt, Brian R.; Girvan, Michelle; Ott, Edward

    2018-04-01

    A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the mechanistic processes governing the dynamics to build an approximate mathematical model of the system. In contrast, machine learning techniques have demonstrated promising results for forecasting chaotic systems purely from past time series measurements of system state variables (training data), without prior knowledge of the system dynamics. The motivation for this paper is the potential of machine learning for filling in the gaps in our underlying mechanistic knowledge that cause widely-used knowledge-based models to be inaccurate. Thus, we here propose a general method that leverages the advantages of these two approaches by combining a knowledge-based model and a machine learning technique to build a hybrid forecasting scheme. Potential applications for such an approach are numerous (e.g., improving weather forecasting). We demonstrate and test the utility of this approach using a particular illustrative version of a machine learning known as reservoir computing, and we apply the resulting hybrid forecaster to a low-dimensional chaotic system, as well as to a high-dimensional spatiotemporal chaotic system. These tests yield extremely promising results in that our hybrid technique is able to accurately predict for a much longer period of time than either its machine-learning component or its model-based component alone.

  15. Model of Cholera Forecasting Using Artificial Neural Network in Chabahar City, Iran

    Directory of Open Access Journals (Sweden)

    Zahra Pezeshki

    2016-02-01

    Full Text Available Background: Cholera as an endemic disease remains a health issue in Iran despite decrease in incidence. Since forecasting epidemic diseases provides appropriate preventive actions in disease spread, different forecasting methods including artificial neural networks have been developed to study parameters involved in incidence and spread of epidemic diseases such as cholera. Objectives: In this study, cholera in rural area of Chabahar, Iran was investigated to achieve a proper forecasting model. Materials and Methods: Data of cholera was gathered from 465 villages, of which 104 reported cholera during ten years period of study. Logistic regression modeling and correlate bivariate were used to determine risk factors and achieve possible predictive model one-hidden-layer perception neural network with backpropagation training algorithm and the sigmoid activation function was trained and tested between the two groups of infected and non-infected villages after preprocessing. For determining validity of prediction, the ROC diagram was used. The study variables included climate conditions and geographical parameters. Results: After determining significant variables of cholera incidence, the described artificial neural network model was capable of forecasting cholera event among villages of test group with accuracy up to 80%. The highest accuracy was achieved when model was trained with variables that were significant in statistical analysis describing that the two methods confirm the result of each other. Conclusions: Application of artificial neural networking assists forecasting cholera for adopting protective measures. For a more accurate prediction, comprehensive information is required including data on hygienic, social and demographic parameters.

  16. Forecasting monthly inflow discharge of the Iffezheim reservoir using data-driven models

    Science.gov (United States)

    Zhang, Qing; Aljoumani, Basem; Hillebrand, Gudrun; Hoffmann, Thomas; Hinkelmann, Reinhard

    2017-04-01

    River stream flow is an essential element in hydrology study fields, especially for reservoir management, since it defines input into reservoirs. Forecasting this stream flow plays an important role in short or long-term planning and management in the reservoir, e.g. optimized reservoir and hydroelectric operation or agricultural irrigation. Highly accurate flow forecasting can significantly reduce economic losses and is always pursued by reservoir operators. Therefore, hydrologic time series forecasting has received tremendous attention of researchers. Many models have been proposed to improve the hydrological forecasting. Due to the fact that most natural phenomena occurring in environmental systems appear to behave in random or probabilistic ways, different cases may need a different methods to forecast the inflow and even a unique treatment to improve the forecast accuracy. The purpose of this study is to determine an appropriate model for forecasting monthly inflow to the Iffezheim reservoir in Germany, which is the last of the barrages in the Upper Rhine. Monthly time series of discharges, measured from 1946 to 2001 at the Plittersdorf station, which is located 6 km downstream of the Iffezheim reservoir, were applied. The accuracies of the used stochastic models - Fiering model and Auto-Regressive Integrated Moving Average models (ARIMA) are compared with Artificial Intelligence (AI) models - single Artificial Neural Network (ANN) and Wavelet ANN models (WANN). The Fiering model is a linear stochastic model and used for generating synthetic monthly data. The basic idea in modeling time series using ARIMA is to identify a simple model with as few model parameters as possible in order to provide a good statistical fit to the data. To identify and fit the ARIMA models, four phase approaches were used: identification, parameter estimation, diagnostic checking, and forecasting. An automatic selection criterion, such as the Akaike information criterion, is utilized

  17. A forecasting model for dengue incidence in the District of Gampaha, Sri Lanka.

    Science.gov (United States)

    Withanage, Gayan P; Viswakula, Sameera D; Nilmini Silva Gunawardena, Y I; Hapugoda, Menaka D

    2018-04-24

    Dengue is one of the major health problems in Sri Lanka causing an enormous social and economic burden to the country. An accurate early warning system can enhance the efficiency of preventive measures. The aim of the study was to develop and validate a simple accurate forecasting model for the District of Gampaha, Sri Lanka. Three time-series regression models were developed using monthly rainfall, rainy days, temperature, humidity, wind speed and retrospective dengue incidences over the period January 2012 to November 2015 for the District of Gampaha, Sri Lanka. Various lag times were analyzed to identify optimum forecasting periods including interactions of multiple lags. The models were validated using epidemiological data from December 2015 to November 2017. Prepared models were compared based on Akaike's information criterion, Bayesian information criterion and residual analysis. The selected model forecasted correctly with mean absolute errors of 0.07 and 0.22, and root mean squared errors of 0.09 and 0.28, for training and validation periods, respectively. There were no dengue epidemics observed in the district during the training period and nine outbreaks occurred during the forecasting period. The proposed model captured five outbreaks and correctly rejected 14 within the testing period of 24 months. The Pierce skill score of the model was 0.49, with a receiver operating characteristic of 86% and 92% sensitivity. The developed weather based forecasting model allows warnings of impending dengue outbreaks and epidemics in advance of one month with high accuracy. Depending upon climatic factors, the previous month's dengue cases had a significant effect on the dengue incidences of the current month. The simple, precise and understandable forecasting model developed could be used to manage limited public health resources effectively for patient management, vector surveillance and intervention programmes in the district.

  18. Air Quality Forecasts Using the NASA GEOS Model: A Unified Tool from Local to Global Scales

    Science.gov (United States)

    Knowland, E. Emma; Keller, Christoph; Nielsen, J. Eric; Orbe, Clara; Ott, Lesley; Pawson, Steven; Saunders, Emily; Duncan, Bryan; Cook, Melanie; Liu, Junhua; hide

    2017-01-01

    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 (approximately 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.

  19. Toward multi-day-ahead forecasting of suspended sediment concentration using ensemble models.

    Science.gov (United States)

    Alizadeh, Mohamad Javad; Jafari Nodoushan, Ehsan; Kalarestaghi, Naghi; Chau, Kwok Wing

    2017-12-01

    This study explores two ideas to made an improvement on the artificial neural network (ANN)-based models for suspended sediment forecasting in several time steps ahead. In this regard, both observed and forecasted time series are incorporated as input variables of the models when applied for more than one lead time. Secondly, least-square ensemble models employing multiple wavelet-ANN models are developed to increase the performance of the single model. For this purpose, different wavelet families are linked with the ANN model and performance of each model is evaluated using error measures. The Skagit River near Mount Vernon in Washington county is selected as the case study. The daily flow discharge and suspended sediment concentration (SSC) in the current day are considered as input variables to predict suspended sediment concentration in the next day. For more lead times, the input structure is updated by adding the forecast of SSC in the previous time step. Results of this study demonstrate that incorporating both observed and predicted variables in the input structure improves performance of conventional models in which those only employ observed time series as input variables. Moreover, ensemble model developed for each lead time outperforms the best single wavelet-ANN model which indicates superiority of the ensemble model over the other one. Findings of this study reveal that acceptable forecasts of daily suspended sediment concentration up to 3 days in advance can be achieved using the proposed methodology.

  20. Use of MLCM3 Software for Flash Flood Modeling and Forecasting

    Directory of Open Access Journals (Sweden)

    Inna Pivovarova

    2018-01-01

    Full Text Available Accurate and timely flash floods forecasting, especially, in ungauged and poorly gauged basins, is one of the most important and challenging problems to be solved by the international hydrological community. In changing climate and variable anthropogenic impact on river basins, as well as due to low density of surface hydrometeorological network, flash flood forecasting based on “traditional” physically based, or conceptual, or statistical hydrological models often becomes inefficient. Unfortunately, most of river basins in Russia are poorly gauged or ungauged; besides, lack of hydrogeological data is quite typical. However, the developing economy and population safety necessitate issuing warnings based on reliable forecasts. For this purpose, a new hydrological model, MLCM3 (Multi-Layer Conceptual Model, 3 rd generation has been developed in the Russian State Hydrometeorological University. The model showed good results in more than 50 tested basins.

  1. Forecasting dengue hemorrhagic fever cases using ARIMA model: a case study in Asahan district

    Science.gov (United States)

    Siregar, Fazidah A.; Makmur, Tri; Saprin, S.

    2018-01-01

    Time series analysis had been increasingly used to forecast the number of dengue hemorrhagic fever in many studies. Since no vaccine exist and poor public health infrastructure, predicting the occurrence of dengue hemorrhagic fever (DHF) is crucial. This study was conducted to determine trend and forecasting the occurrence of DHF in Asahan district, North Sumatera Province. Monthly reported dengue cases for the years 2012-2016 were obtained from the district health offices. A time series analysis was conducted by Autoregressive integrated moving average (ARIMA) modeling to forecast the occurrence of DHF. The results demonstrated that the reported DHF cases showed a seasonal variation. The SARIMA (1,0,0)(0,1,1)12 model was the best model and adequate for the data. The SARIMA model for DHF is necessary and could applied to predict the incidence of DHF in Asahan district and assist with design public health maesures to prevent and control the diseases.

  2. 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.

  3. Structural Forecasts for the Danish Economy Using The Dynamic-AAGE Model

    DEFF Research Database (Denmark)

    Adams, Philip D; Andersen, Lill Thanning; Jacobsen, Lars Bo

    2002-01-01

    Dynamic-AAGE model is the dynamic version of the Agricultural Applied General Equilibrium (AAGE) model of the Danish economy. Each solution of Dynamic-AAGE produces a picture of the Danish economy at a high level of detail for a particular year. The model can also produce a sequence of annual...... solutions, linked together by ensuring, for example, that the quantities of opening capital stocks in any year equal the quantities of closing stocks in the previous year. Traditionally, applied general equilibrium (AGE) models like Dynamic-AAGE have been used to answer "what if" questions such as: how...... developed for MONASH has now been applied to Dynamic-AAGE to generate structural forecasts for the Danish economy. The starting point for the Dynamic-AAGE forecasts is a set of scenarios for the macro economy supplied by a specialist-forecasting agency, currently the Danish Economic Council. Expert...

  4. Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model

    Science.gov (United States)

    Yaseen, Zaher Mundher; Ebtehaj, Isa; Bonakdari, Hossein; Deo, Ravinesh C.; Danandeh Mehr, Ali; Mohtar, Wan Hanna Melini Wan; Diop, Lamine; El-shafie, Ahmed; Singh, Vijay P.

    2017-11-01

    The present study proposes a new hybrid evolutionary Adaptive Neuro-Fuzzy Inference Systems (ANFIS) approach for monthly streamflow forecasting. The proposed method is a novel combination of the ANFIS model with the firefly algorithm as an optimizer tool to construct a hybrid ANFIS-FFA model. The results of the ANFIS-FFA model is compared with the classical ANFIS model, which utilizes the fuzzy c-means (FCM) clustering method in the Fuzzy Inference Systems (FIS) generation. The historical monthly streamflow data for Pahang River, which is a major river system in Malaysia that characterized by highly stochastic hydrological patterns, is used in the study. Sixteen different input combinations with one to five time-lagged input variables are incorporated into the ANFIS-FFA and ANFIS models to consider the antecedent seasonal variations in historical streamflow data. The mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (r) are used to evaluate the forecasting performance of ANFIS-FFA model. In conjunction with these metrics, the refined Willmott's Index (Drefined), Nash-Sutcliffe coefficient (ENS) and Legates and McCabes Index (ELM) are also utilized as the normalized goodness-of-fit metrics. Comparison of the results reveals that the FFA is able to improve the forecasting accuracy of the hybrid ANFIS-FFA model (r = 1; RMSE = 0.984; MAE = 0.364; ENS = 1; ELM = 0.988; Drefined = 0.994) applied for the monthly streamflow forecasting in comparison with the traditional ANFIS model (r = 0.998; RMSE = 3.276; MAE = 1.553; ENS = 0.995; ELM = 0.950; Drefined = 0.975). The results also show that the ANFIS-FFA is not only superior to the ANFIS model but also exhibits a parsimonious modelling framework for streamflow forecasting by incorporating a smaller number of input variables required to yield the comparatively better performance. It is construed that the FFA optimizer can thus surpass the accuracy of the traditional ANFIS model in general

  5. 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.

  6. A model for recovery kinetics of aluminum after large strain

    DEFF Research Database (Denmark)

    Yu, Tianbo; Hansen, Niels

    2012-01-01

    A model is suggested to analyze recovery kinetics of heavily deformed aluminum. The model is based on the hardness of isothermal annealed samples before recrystallization takes place, and it can be extrapolated to longer annealing times to factor out the recrystallization component of the hardness...... for conditions where recovery and recrystallization overlap. The model is applied to the isothermal recovery at temperatures between 140 and 220°C of commercial purity aluminum deformed to true strain 5.5. EBSD measurements have been carried out to detect the onset of discontinuous recrystallization. Furthermore...

  7. A new wind speed forecasting strategy based on the chaotic time series modelling technique and the Apriori algorithm

    International Nuclear Information System (INIS)

    Guo, Zhenhai; Chi, Dezhong; Wu, Jie; Zhang, Wenyu

    2014-01-01

    Highlights: • Impact of meteorological factors on wind speed forecasting is taken into account. • Forecasted wind speed results are corrected by the associated rules. • Forecasting accuracy is improved by the new wind speed forecasting strategy. • Robust of the proposed model is validated by data sampled from different sites. - Abstract: Wind energy has been the fastest growing renewable energy resource in recent years. Because of the intermittent nature of wind, wind power is a fluctuating source of electrical energy. Therefore, to minimize the impact of wind power on the electrical grid, accurate and reliable wind power forecasting is mandatory. In this paper, a new wind speed forecasting approach based on based on the chaotic time series modelling technique and the Apriori algorithm has been developed. The new approach consists of four procedures: (I) Clustering by using the k-means clustering approach; (II) Employing the Apriori algorithm to discover the association rules; (III) Forecasting the wind speed according to the chaotic time series forecasting model; and (IV) Correcting the forecasted wind speed data using the associated rules discovered previously. This procedure has been verified by 31-day-ahead daily average wind speed forecasting case studies, which employed the wind speed and other meteorological data collected from four meteorological stations located in the Hexi Corridor area of China. The results of these case studies reveal that the chaotic forecasting model can efficiently improve the accuracy of the wind speed forecasting, and the Apriori algorithm can effectively discover the association rules between the wind speed and other meteorological factors. In addition, the correction results demonstrate that the association rules discovered by the Apriori algorithm have powerful capacities in handling the forecasted wind speed values correction when the forecasted values do not match the classification discovered by the association rules

  8. Least square regression based integrated multi-parameteric demand modeling for short term load forecasting

    International Nuclear Information System (INIS)

    Halepoto, I.A.; Uqaili, M.A.

    2014-01-01

    Nowadays, due to power crisis, electricity demand forecasting is deemed an important area for socioeconomic development and proper anticipation of the load forecasting is considered essential step towards efficient power system operation, scheduling and planning. In this paper, we present STLF (Short Term Load Forecasting) using multiple regression techniques (i.e. linear, multiple linear, quadratic and exponential) by considering hour by hour load model based on specific targeted day approach with temperature variant parameter. The proposed work forecasts the future load demand correlation with linear and non-linear parameters (i.e. considering temperature in our case) through different regression approaches. The overall load forecasting error is 2.98% which is very much acceptable. From proposed regression techniques, Quadratic Regression technique performs better compared to than other techniques because it can optimally fit broad range of functions and data sets. The work proposed in this paper, will pave a path to effectively forecast the specific day load with multiple variance factors in a way that optimal accuracy can be maintained. (author)

  9. Implementation of bayesian model averaging on the weather data forecasting applications utilizing open weather map

    Science.gov (United States)

    Rahmat, R. F.; Nasution, F. R.; Seniman; Syahputra, M. F.; Sitompul, O. S.

    2018-02-01

    Weather is condition of air in a certain region at a relatively short period of time, measured with various parameters such as; temperature, air preasure, wind velocity, humidity and another phenomenons in the atmosphere. In fact, extreme weather due to global warming would lead to drought, flood, hurricane and other forms of weather occasion, which directly affects social andeconomic activities. Hence, a forecasting technique is to predict weather with distinctive output, particullary mapping process based on GIS with information about current weather status in certain cordinates of each region with capability to forecast for seven days afterward. Data used in this research are retrieved in real time from the server openweathermap and BMKG. In order to obtain a low error rate and high accuracy of forecasting, the authors use Bayesian Model Averaging (BMA) method. The result shows that the BMA method has good accuracy. Forecasting error value is calculated by mean square error shows (MSE). The error value emerges at minumum temperature rated at 0.28 and maximum temperature rated at 0.15. Meanwhile, the error value of minimum humidity rates at 0.38 and the error value of maximum humidity rates at 0.04. Afterall, the forecasting error rate of wind speed is at 0.076. The lower the forecasting error rate, the more optimized the accuracy is.

  10. Results of the Regional Earthquake Likelihood Models (RELM) test of earthquake forecasts in California.

    Science.gov (United States)

    Lee, Ya-Ting; Turcotte, Donald L; Holliday, James R; Sachs, Michael K; Rundle, John B; Chen, Chien-Chih; Tiampo, Kristy F

    2011-10-04

    The Regional Earthquake Likelihood Models (RELM) test of earthquake forecasts in California was the first competitive evaluation of forecasts of future earthquake occurrence. Participants submitted expected probabilities of occurrence of M ≥ 4.95 earthquakes in 0.1° × 0.1° cells for the period 1 January 1, 2006, to December 31, 2010. Probabilities were submitted for 7,682 cells in California and adjacent regions. During this period, 31 M ≥ 4.95 earthquakes occurred in the test region. These earthquakes occurred in 22 test cells. This seismic activity was dominated by earthquakes associated with the M = 7.2, April 4, 2010, El Mayor-Cucapah earthquake in northern Mexico. This earthquake occurred in the test region, and 16 of the other 30 earthquakes in the test region could be associated with it. Nine complete forecasts were submitted by six participants. In this paper, we present the forecasts in a way that allows the reader to evaluate which forecast is the most "successful" in terms of the locations of future earthquakes. We conclude that the RELM test was a success and suggest ways in which the results can be used to improve future forecasts.

  11. A regime-switching stochastic volatility model for forecasting electricity prices

    DEFF Research Database (Denmark)

    Exterkate, Peter; Knapik, Oskar

    In a recent review paper, Weron (2014) pinpoints several crucial challenges outstanding in the area of electricity price forecasting. This research attempts to address all of them by i) showing the importance of considering fundamental price drivers in modeling, ii) developing new techniques...... for probabilistic (i.e. interval or density) forecasting of electricity prices, iii) introducing an universal technique for model comparison. We propose new regime-switching stochastic volatility model with three regimes (negative jump, normal price, positive jump (spike)) where the transition matrix depends...

  12. An Experimental Investigation of FNN Model for Wind Speed Forecasting Using EEMD and CS

    Directory of Open Access Journals (Sweden)

    Jianzhou Wang

    2015-01-01

    Full Text Available With depletion of traditional energy and increasing environmental problems, wind energy, as an alternative renewable energy, has drawn more and more attention internationally. Meanwhile, wind is plentiful, clean, and environmentally friendly; moreover, its speed is a very important piece of information needed in the operations and planning of the wind power system. Therefore, choosing an effective forecasting model with good performance plays a quite significant role in wind power system. A hybrid CS-EEMD-FNN model is firstly proposed in this paper for multistep ahead prediction of wind speed, in which EEMD is employed as a data-cleaning method that aims to remove the high frequency noise embedded in the wind speed series. CS optimization algorithm is used to select the best parameters in the FNN model. In order to evaluate the effectiveness and performance of the proposed hybrid model, three other short-term wind speed forecasting models, namely, FNN model, EEMD-FNN model, and CS-FNN model, are carried out to forecast wind speed using data measured at a typical site in Shandong wind farm, China, over three seasons in 2011. Experimental results demonstrate that the developed hybrid CS-EEMD-FNN model outperforms other models with more accuracy, which is suitable to wind speed forecasting in this area.

  13. Comparing univariate techniques for tender price index forecasting: Box-Jenkins and neural network model

    Directory of Open Access Journals (Sweden)

    Olalekan Oshodi

    2017-09-01

    Full Text Available The poor performance of projects is a recurring event in the construction sector. Information gleaned from literature shows that uncertainty in project cost is one of the significant causes of this problem. Reliable forecast of construction cost is useful in mitigating the adverse effect of its fluctuation, however the availability of data for the development of multivariate models for construction cost forecasting remains a challenge. The study seeks to investigate the reliability of using univariate models for tender price index forecasting. Box-Jenkins and neural network are the modelling techniques applied in this study. The results show that the neural network model outperforms the Box-Jenkins model, in terms of accuracy. In addition, the neural network model provides a reliable forecast of tender price index over a period of 12 quarters ahead. The limitations of using the univariate models are elaborated. The developed neural network model can be used by stakeholders as a tool for predicting the movements in tender price index. In addition, the univariate models developed in the present study are particularly useful in countries where limited data reduces the possibility of applying multivariate models.

  14. Application of semi parametric modelling to times series forecasting: case of the electricity consumption

    International Nuclear Information System (INIS)

    Lefieux, V.

    2007-10-01

    Reseau de Transport d'Electricite (RTE), in charge of operating the French electric transportation grid, needs an accurate forecast of the power consumption in order to operate it correctly. The forecasts used everyday result from a model combining a nonlinear parametric regression and a SARIMA model. In order to obtain an adaptive forecasting model, nonparametric forecasting methods have already been tested without real success. In particular, it is known that a nonparametric predictor behaves badly with a great number of explanatory variables, what is commonly called the curse of dimensionality. Recently, semi parametric methods which improve the pure nonparametric approach have been proposed to estimate a regression function. Based on the concept of 'dimension reduction', one those methods (called MAVE : Moving Average -conditional- Variance Estimate) can apply to time series. We study empirically its effectiveness to predict the future values of an autoregressive time series. We then adapt this method, from a practical point of view, to forecast power consumption. We propose a partially linear semi parametric model, based on the MAVE method, which allows to take into account simultaneously the autoregressive aspect of the problem and the exogenous variables. The proposed estimation procedure is practically efficient. (author)

  15. Development of a Statistical Model for Forecasting Episodes of Visibility Degradation in the Denver Metropolitan Area.

    Science.gov (United States)

    Reddy, P. J.; Barbarick, D. E.; Osterburg, R. D.

    1995-03-01

    In 1990, the State of Colorado implemented a visibility standard of 0.076 km1 of beta extinction for the Denver metropolitan area. Meteorologists with Colorado's Air Pollution Control Division forecast high pollution days associated with visibility impairment as well as those due to high levels of the federal criteria pollutants. Visibility forecasts are made from a few hours up to about 26 h in advance of the period of interest. Here we discuss the key microscale, mesoscale, and synoptic-scale features associated with episodes of visibility impairment. Data from special studies, case studies, and the 22 NOAA Program for Regional Observing and Forecasting Services mesonet sites have been invaluable in identifying patterns associated with extremes in visibility conditions. A preliminary statistical forecast model has been developed using variables that represent many of these patterns. Six variables were selected from an initial pool of 27 to be used in a model based on linear logistic regression. These six variables include forecast measures of snow cover, surface pressures and a surface pressure gradient in eastern Colorado, relative humidity, and 500-mb ridge position. The initial testing of the model has been encouraging. The model correctly predicted 76% of the good visibility days and 67% of the poor visibility days for a test set of 171 days.

  16. Forecasting the natural gas demand in China using a self-adapting intelligent grey model

    International Nuclear Information System (INIS)

    Zeng, Bo; Li, Chuan

    2016-01-01

    Reasonably forecasting demands of natural gas in China is of significance as it could aid Chinese government in formulating energy policies and adjusting industrial structures. To this end, a self-adapting intelligent grey prediction model is proposed in this paper. Compared with conventional grey models which have the inherent drawbacks of fixed structure and poor adaptability, the proposed new model can automatically optimize model parameters according to the real data characteristics of modeling sequence. In this study, the proposed new model, discrete grey model, even difference grey model and classical grey model were employed, respectively, to simulate China's natural gas demands during 2002–2010 and forecast demands during 2011–2014. The results show the new model has the best simulative and predictive precision. Finally, the new model is used to forecast China's natural gas demand during 2015–2020. The forecast shows the demand will grow rapidly over the next six years. Therefore, in order to maintain the balance between the supplies and the demands for the natural gas in the future, Chinese government needs to take some measures, such as importing huge amounts of natural gas from abroad, increasing the domestic yield, using more alternative energy, and reducing the industrial reliance on natural gas. - Highlights: • A self-adapting intelligent grey prediction model (SIGM) is proposed in this paper. • The SIGM has the advantage of working with exponential functions and linear functions. • The SIGM solves the drawbacks of fixed structure and poor adaptability of grey models. • The demand of natural gas in China is successfully forecasted using the SIGM model. • The study findings can help Chinese government reasonably formulate energy policies.

  17. [Medical human resources planning in Europe: A literature review of the forecasting models].

    Science.gov (United States)

    Benahmed, N; Deliège, D; De Wever, A; Pirson, M

    2018-02-01

    Healthcare is a labor-intensive sector in which half of the expenses are dedicated to human resources. Therefore, policy makers, at national and internal levels, attend to the number of practicing professionals and the skill mix. This paper aims to analyze the European forecasting model for supply and demand of physicians. To describe the forecasting tools used for physician planning in Europe, a grey literature search was done in the OECD, WHO, and European Union libraries. Electronic databases such as Pubmed, Medine, Embase and Econlit were also searched. Quantitative methods for forecasting medical supply rely mainly on stock-and-flow simulations and less often on systemic dynamics. Parameters included in forecasting models exhibit wide variability for data availability and quality. The forecasting of physician needs is limited to healthcare consumption and rarely considers overall needs and service targets. Besides quantitative methods, horizon scanning enables an evaluation of the changes in supply and demand in an uncertain future based on qualitative techniques such as semi-structured interviews, Delphi Panels, or focus groups. Finally, supply and demand forecasting models should be regularly updated. Moreover, post-hoc analyze is also needed but too rarely implemented. Medical human resource planning in Europe is inconsistent. Political implementation of the results of forecasting projections is essential to insure efficient planning. However, crucial elements such as mobility data between Member States are poorly understood, impairing medical supply regulation policies. These policies are commonly limited to training regulations, while horizontal and vertical substitution is less frequently taken into consideration. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

  18. 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

  19. FLOOD FORECASTING MODEL USING EMPIRICAL METHOD FOR A SMALL CATCHMENT AREA

    Directory of Open Access Journals (Sweden)

    CHANG L. JUN

    2016-05-01

    Full Text Available The two most destructive natural disasters in Malaysia are monsoonal and flash floods. Malaysia is located in the tropical area and received on average, around 2800 mm of rainfall every year. Due to this high amount, a reliable and timely flood forecasting system is necessary to provide early warning to minimize the destruction caused by flash flood. This study developed and checked the adaptability and adequacy of the flood forecasting model for 93 km2 catchment area, Kampung Kasipillay, in Kuala Lumpur. The Empirical Unit Hydrograph Model was used in this study and past rainfall data, water level and stagedischarge curve were used as inputs. A Rainfall-Runoff Model (RRM which transforms the rainfall to runoff hydrograph, was developed using excel. Since some data, such as properties of the watershed, are not always complete and precise, some model parameters were calibrated through trial and error processes to fine-tune the parameters of the model to get reliable estimation. The simulated unit hydrograph model was computed in prior runs of the flood forecasting model to estimate the model parameters. These calibrated parameters are used as constant variables for flood forecasting model when the runoff hydrograph was regenerated. The comparison between the observed and simulated hydrograph was investigated for the selected flood events and performance error was determined. The performance error achieved in this study of 15 flood events ranged from -2.06% to 5.82%.e.

  20. Development of S-ARIMA Model for Forecasting Demand in a Beverage Supply Chain

    Science.gov (United States)

    Mircetic, Dejan; Nikolicic, Svetlana; Maslaric, Marinko; Ralevic, Nebojsa; Debelic, Borna

    2016-11-01

    Demand forecasting is one of the key activities in planning the freight flows in supply chains, and accordingly it is essential for planning and scheduling of logistic activities within observed supply chain. Accurate demand forecasting models directly influence the decrease of logistics costs, since they provide an assessment of customer demand. Customer demand is a key component for planning all logistic processes in supply chain, and therefore determining levels of customer demand is of great interest for supply chain managers. In this paper we deal with exactly this kind of problem, and we develop the seasonal Autoregressive IntegratedMoving Average (SARIMA) model for forecasting demand patterns of a major product of an observed beverage company. The model is easy to understand, flexible to use and appropriate for assisting the expert in decision making process about consumer demand in particular periods.