Forecasting ocean wave energy: A Comparison of the ECMWF wave model with time series methods
DEFF Research Database (Denmark)
Reikard, Gordon; Pinson, Pierre; Bidlot, Jean
2011-01-01
Recently, the technology has been developed to make wave farms commercially viable. Since electricity is perishable, utilities will be interested in forecasting ocean wave energy. The horizons involved in short-term management of power grids range from as little as a few hours to as long as several...... days. In selecting a method, the forecaster has a choice between physics-based models and statistical techniques. A further idea is to combine both types of models. This paper analyzes the forecasting properties of a well-known physics-based model, the European Center for Medium-Range Weather Forecasts...... (ECMWF) Wave Model, and two statistical techniques, time-varying parameter regressions and neural networks. Thirteen data sets at locations in the Atlantic and Pacific Oceans and the Gulf of Mexico are tested. The quantities to be predicted are the significant wave height, the wave period, and the wave...
Recurrent networks for wave forecasting
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.; Prabaharan, N.
, merchant vessel routing, nearshore construction, etc. more efficiently and safely. This paper presents an application of the Artificial Neural Network, namely Backpropagation Recurrent Neural Network (BRNN) with rprop update algorithm for wave forecasting...
Up-Wave and Autoregressive Methods for Short-Term Wave Forecasting for an Oscillating Water Column
Paparella, Francesco; Monk, Kieran; Winands, Victor; Lopes, M.F.P.; Conley, Daniel; Ringwood, John
2015-01-01
The real-time control of wave energy converters (WECs) requires the prediction of the wave elevation at the location of the device in order to maximize the power extracted from the waves. One possibility is to predict the future wave elevation by combining its past history with the spatial information coming from a sensor which measures the free surface elevation up-wave of the WEC. As an application example, this paper focuses on the prediction of the wave elevation inside the chamber of the...
Ocean wave forecasting using recurrent neural networks
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.; Prabaharan, N.
, merchant vessel routing, nearshore construction, etc. more efficiently and safely. This paper describes an artificial neural network, namely recurrent neural network with rprop update algorithm and is applied for wave forecasting. Measured ocean waves off...
Assimilation of Wave Imaging Radar Observations for Real-Time Wave-by-Wave Forecasting
Haller, M. C.; Simpson, A. J.; Walker, D. T.; Lynett, P. J.; Pittman, R.; Honegger, D.
2016-02-01
It has been shown in various studies that a controls system can dramatically improve Wave Energy Converter (WEC) power production by tuning the device's oscillations to the incoming wave field, as well as protect WEC devices by decoupling them in extreme wave conditions. A requirement of the most efficient controls systems is a phase-resolved, "deterministic" surface elevation profile, alerting the device to what it will experience in the near future. The current study aims to demonstrate a deterministic method of wave forecasting through the pairing of an X-Band marine radar with a predictive Mild Slope Equation (MSE) wave model. Using the radar as a remote sensing technique, the wave field up to 1-4 km surrounding a WEC device can be resolved. Individual waves within the radar scan are imaged through the contrast between high intensity wave faces and low intensity wave troughs. Using a recently developed method, radar images are inverted into the radial component of surface slope, shown in the figure provided using radar data from Newport, Oregon. Then, resolved radial slope images are assimilated into the MSE wave model. This leads to a best-fit model hindcast of the waves within the domain. The hindcast is utilized as an initial condition for wave-by-wave forecasting with a target forecast horizon of 3-5 minutes (tens of wave periods). The methodology is currently being tested with synthetic data and comparisons with field data are imminent.
Uranium price forecasting methods
International Nuclear Information System (INIS)
Fuller, D.M.
1994-01-01
This article reviews a number of forecasting methods that have been applied to uranium prices and compares their relative strengths and weaknesses. The methods reviewed are: (1) judgemental methods, (2) technical analysis, (3) time-series methods, (4) fundamental analysis, and (5) econometric methods. Historically, none of these methods has performed very well, but a well-thought-out model is still useful as a basis from which to adjust to new circumstances and try again
Statistical methods for forecasting
Abraham, Bovas
2009-01-01
The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists."This book, it must be said, lives up to the words on its advertising cover: ''Bridging the gap between introductory, descriptive approaches and highly advanced theoretical treatises, it provides a practical, intermediate level discussion of a variety of forecasting tools, and explains how they relate to one another, both in theory and practice.'' It does just that!"-Journal of the Royal Statistical Society"A well-written work that deals with statistical methods and models that can be used to produce short-term forecasts, this book has wide-ranging applications. It could be used in the context of a study of regression, forecasting, and time series ...
Wave forecasting in near real time basis by neural network
Digital Repository Service at National Institute of Oceanography (India)
Rao, S.; Mandal, S.; Prabaharan, N.
., forecasting of waves become an important aspect of marine environment. This paper presents application of the neural network (NN) with better update algorithms, namely rprop, quickprop and superSAB for wave forecasting. Measured waves off Marmagoa, Goa, India...
Whitford, Dennis J.
2002-05-01
Ocean waves are the most recognized phenomena in oceanography. Unfortunately, undergraduate study of ocean wave dynamics and forecasting involves mathematics and physics and therefore can pose difficulties with some students because of the subject's interrelated dependence on time and space. Verbal descriptions and two-dimensional illustrations are often insufficient for student comprehension. Computer-generated visualization and animation offer a visually intuitive and pedagogically sound medium to present geoscience, yet there are very few oceanographic examples. A two-part article series is offered to explain ocean wave forecasting using computer-generated visualization and animation. This paper, Part 1, addresses forecasting of sea wave conditions and serves as the basis for the more difficult topic of swell wave forecasting addressed in Part 2. Computer-aided visualization and animation, accompanied by oral explanation, are a welcome pedagogical supplement to more traditional methods of instruction. In this article, several MATLAB ® software programs have been written to visualize and animate development and comparison of wave spectra, wave interference, and forecasting of sea conditions. These programs also set the stage for the more advanced and difficult animation topics in Part 2. The programs are user-friendly, interactive, easy to modify, and developed as instructional tools. By using these software programs, teachers can enhance their instruction of these topics with colorful visualizations and animation without requiring an extensive background in computer programming.
System of the Wind Wave Operational Forecast by the Black Sea Marine Forecast Center
Directory of Open Access Journals (Sweden)
Yu.B. Ratner
2017-10-01
Full Text Available System of the wind wave operational forecast in the Black Sea based on the SWAN (Simulating Waves Nearshore numerical spectral model is represented. In the course of the system development the SWAN model was adapted to take into account the features of its operation at the Black Sea Marine Forecast Center. The model input-output is agreed with the applied nomenclature and the data representation formats. The user interface for rapid access to simulation results was developed. The model adapted to wave forecast in the Black Sea in a quasi-operational mode, is validated for 2012–2015. Validation of the calculation results was carried out for all five forecasting terms based on the analysis of two-dimensional graphs of the wave height distribution derived from the data of prognostic calculations and remote measurements obtained with the altimeter installed on the Jason-2 satellite. Calculation of the statistical characteristics of the deviations between the wave height prognostic values and the data of their measurements from the Jason-2 satellite, as well as a regression analysis of the relationship between the forecasted and measured wave heights was additionally carried out. A comparison of the results obtained with the similar results reported in the works of other authors published in 2009–2016 showed their satisfactory compliance with each other. The forecasts carried out by the authors for the Black Sea as well as those obtained for the other World Ocean regions show that the current level of numerical methods for sea wave forecasting is in full compliance with the requirements of specialists engaged in studying and modeling the state of the ocean and the atmosphere, as well as the experts using these results for solving applied problems.
Energy forecasts, perspectives and methods
Energy Technology Data Exchange (ETDEWEB)
Svensson, J E; Mogren, A
1984-01-01
The authors have analyzed different methods for long term energy prognoses, in particular energy consumption forecasts. Energy supply and price prognoses are also treated, but in a less detailed manner. After defining and discussing the various methods/models used in forecasts, a generalized discussion of the influence on the prognoses from the perspectives (background factors, world view, norms, ideology) of the prognosis makers is given. Some basic formal demands that should be asked from any rational forecast are formulated and discussed. The authors conclude that different forecasting methodologies are supplementing each other. There is no best method, forecasts should be accepted as views of the future from differing perspectives. The primary prognostic problem is to show the possible futures, selecting the wanted future is a question of political process.
Probabilistic Forecasting of the Wave Energy Flux
DEFF Research Database (Denmark)
Pinson, Pierre; Reikard, G.; Bidlot, J.-R.
2012-01-01
Wave energy will certainly have a significant role to play in the deployment of renewable energy generation capacities. As with wind and solar, probabilistic forecasts of wave power over horizons of a few hours to a few days are required for power system operation as well as trading in electricit......% and 70% in terms of Continuous Rank Probability Score (CRPS), depending upon the test case and the lead time. It is finally shown that the log-Normal assumption can be seen as acceptable, even though it may be refined in the future....
Method of forecasting power distribution
International Nuclear Information System (INIS)
Kaneto, Kunikazu.
1981-01-01
Purpose: To obtain forecasting results at high accuracy by reflecting the signals from neutron detectors disposed in the reactor core on the forecasting results. Method: An on-line computer transfers, to a simulator, those process data such as temperature and flow rate for coolants in each of the sections and various measuring signals such as control rod positions from the nuclear reactor. The simulator calculates the present power distribution before the control operation. The signals from the neutron detectors at each of the positions in the reactor core are estimated from the power distribution and errors are determined based on the estimated values and the measured values to determine the smooth error distribution in the axial direction. Then, input conditions at the time to be forecast are set by a data setter. The simulator calculates the forecast power distribution after the control operation based on the set conditions. The forecast power distribution is corrected using the error distribution. (Yoshino, Y.)
Whitford, Dennis J.
2002-05-01
This paper, the second of a two-part series, introduces undergraduate students to ocean wave forecasting using interactive computer-generated visualization and animation. Verbal descriptions and two-dimensional illustrations are often insufficient for student comprehension. Fortunately, the introduction of computers in the geosciences provides a tool for addressing this problem. Computer-generated visualization and animation, accompanied by oral explanation, have been shown to be a pedagogical improvement to more traditional methods of instruction. Cartographic science and other disciplines using geographical information systems have been especially aggressive in pioneering the use of visualization and animation, whereas oceanography has not. This paper will focus on the teaching of ocean swell wave forecasting, often considered a difficult oceanographic topic due to the mathematics and physics required, as well as its interdependence on time and space. Several MATLAB ® software programs are described and offered to visualize and animate group speed, frequency dispersion, angular dispersion, propagation, and wave height forecasting of deep water ocean swell waves. Teachers may use these interactive visualizations and animations without requiring an extensive background in computer programming.
Forecasting Water Waves and Currents: A Space-time Approach
Ambati, V.R.
2008-01-01
Forecasting water waves and currents in near shore and off shore regions of the seas and oceans is essential to maintain and protect our environment and man made structures. In wave hydrodynamics, waves can be classified as shallow and deep water waves based on its water depth. The mathematical
Improving wave forecasting by integrating ensemble modelling and machine learning
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.
Bayesian flood forecasting methods: A review
Han, Shasha; Coulibaly, Paulin
2017-08-01
Over the past few decades, floods have been seen as one of the most common and largely distributed natural disasters in the world. If floods could be accurately forecasted in advance, then their negative impacts could be greatly minimized. It is widely recognized that quantification and reduction of uncertainty associated with the hydrologic forecast is of great importance for flood estimation and rational decision making. Bayesian forecasting system (BFS) offers an ideal theoretic framework for uncertainty quantification that can be developed for probabilistic flood forecasting via any deterministic hydrologic model. It provides suitable theoretical structure, empirically validated models and reasonable analytic-numerical computation method, and can be developed into various Bayesian forecasting approaches. This paper presents a comprehensive review on Bayesian forecasting approaches applied in flood forecasting from 1999 till now. The review starts with an overview of fundamentals of BFS and recent advances in BFS, followed with BFS application in river stage forecasting and real-time flood forecasting, then move to a critical analysis by evaluating advantages and limitations of Bayesian forecasting methods and other predictive uncertainty assessment approaches in flood forecasting, and finally discusses the future research direction in Bayesian flood forecasting. Results show that the Bayesian flood forecasting approach is an effective and advanced way for flood estimation, it considers all sources of uncertainties and produces a predictive distribution of the river stage, river discharge or runoff, thus gives more accurate and reliable flood forecasts. Some emerging Bayesian forecasting methods (e.g. ensemble Bayesian forecasting system, Bayesian multi-model combination) were shown to overcome limitations of single model or fixed model weight and effectively reduce predictive uncertainty. In recent years, various Bayesian flood forecasting approaches have been
Wave energy potential: A forecasting system for the Mediterranean basin
International Nuclear Information System (INIS)
Carillo, Adriana; Sannino, Gianmaria; Lombardi, Emanuele
2015-01-01
ENEA is performing ocean wave modeling activities with the aim of both characterizing the Italian sea energy resource and providing the information necessary for the experimental at sea and operational phases of energy converters. Therefore a forecast system of sea waves and of the associated energy available has been developed and has been operatively running since June 2013. The forecasts are performed over the entire Mediterranean basin and, at a higher resolution, over ten sub-basins around the Italian coasts. The forecast system is here described along with the validation of the wave heights, performed by comparing them with the measurements from satellite sensors. [it
Assimilation of Wave Imaging Radar Observations for Real-time Wave-by-Wave Forecasting
Energy Technology Data Exchange (ETDEWEB)
Simpson, Alexandra [Oregon State Univ., Corvallis, OR (United States); Haller, Merrick [Oregon State Univ., Corvallis, OR (United States). School of Civil & Construction Engineering; Walker, David [SRI International, Menlo Park, CA (United States); Lynett, Pat [Univ. of Southern California, Los Angeles, CA (United States)
2017-08-29
This project addressed Topic 3: “Wave Measurement Instrumentation for Feed Forward Controls” under the FOA number DE-FOA-0000971. The overall goal of the program was to develop a phase-resolving wave forecasting technique for application to the active control of Wave Energy Conversion (WEC) devices. We have developed an approach that couples a wave imaging marine radar with a phase-resolving linear wave model for real-time wave field reconstruction and forward propagation of the wave field in space and time. The scope of the project was to develop and assess the performance of this novel forecasting system. Specific project goals were as follows: Develop and verify a fast, GPU-based (Graphical Processing Unit) wave propagation model suitable for phase-resolved computation of nearshore wave transformation over variable bathymetry; Compare the accuracy and speed of performance of the wave model against a deep water model in their ability to predict wave field transformation in the intermediate water depths (50 to 70 m) typical of planned WEC sites; Develop and implement a variational assimilation algorithm that can ingest wave imaging radar observations and estimate the time-varying wave conditions offshore of the domain of interest such that the observed wave field is best reconstructed throughout the domain and then use this to produce model forecasts for a given WEC location; Collect wave-resolving marine radar data, along with relevant in situ wave data, at a suitable wave energy test site, apply the algorithm to the field data, assess performance, and identify any necessary improvements; and Develop a production cost estimate that addresses the affordability of the wave forecasting technology and include in the Final Report. The developed forecasting algorithm (“Wavecast”) was evaluated for both speed and accuracy against a substantial synthetic dataset. Early in the project, performance tests definitively demonstrated that the system was capable of
Multiresolution wavelet-ANN model for significant wave height forecasting.
Digital Repository Service at National Institute of Oceanography (India)
Deka, P.C.; Mandal, S.; Prahlada, R.
Hybrid wavelet artificial neural network (WLNN) has been applied in the present study to forecast significant wave heights (Hs). Here Discrete Wavelet Transformation is used to preprocess the time series data (Hs) prior to Artificial Neural Network...
Methods and Techniques of Enrollment Forecasting.
Brinkman, Paul T.; McIntyre, Chuck
1997-01-01
There is no right way to forecast college enrollments; in many instances, it will be prudent to use both qualitative and quantitative methods. Methods chosen must be relevant to questions addressed, policies and decisions at stake, and time and talent required. While it is tempting to start quickly, enrollment forecasting is an area in which…
A Machine LearningFramework to Forecast Wave Conditions
Zhang, Y.; James, S. C.; O'Donncha, F.
2017-12-01
Recently, significant effort has been undertaken to quantify and extract wave energy because it is renewable, environmental friendly, abundant, and often close to population centers. However, a major challenge is the ability to accurately and quickly predict energy production, especially across a 48-hour cycle. Accurate forecasting of wave conditions is a challenging undertaking that typically involves solving the spectral action-balance equation on a discretized grid with high spatial resolution. The nature of the computations typically demands high-performance computing infrastructure. Using a case-study site at Monterey Bay, California, a machine learning framework was trained to replicate numerically simulated wave conditions at a fraction of the typical computational cost. Specifically, the physics-based Simulating WAves Nearshore (SWAN) model, driven by measured wave conditions, nowcast ocean currents, and wind data, was used to generate training data for machine learning algorithms. The model was run between April 1st, 2013 and May 31st, 2017 generating forecasts at three-hour intervals yielding 11,078 distinct model outputs. SWAN-generated fields of 3,104 wave heights and a characteristic period could be replicated through simple matrix multiplications using the mapping matrices from machine learning algorithms. In fact, wave-height RMSEs from the machine learning algorithms (9 cm) were less than those for the SWAN model-verification exercise where those simulations were compared to buoy wave data within the model domain (>40 cm). The validated machine learning approach, which acts as an accurate surrogate for the SWAN model, can now be used to perform real-time forecasts of wave conditions for the next 48 hours using available forecasted boundary wave conditions, ocean currents, and winds. This solution has obvious applications to wave-energy generation as accurate wave conditions can be forecasted with over a three-order-of-magnitude reduction in
A Time Series Forecasting Method
Directory of Open Access Journals (Sweden)
Wang Zhao-Yu
2017-01-01
Full Text Available This paper proposes a novel time series forecasting method based on a weighted self-constructing clustering technique. The weighted self-constructing clustering processes all the data patterns incrementally. If a data pattern is not similar enough to an existing cluster, it forms a new cluster of its own. However, if a data pattern is similar enough to an existing cluster, it is removed from the cluster it currently belongs to and added to the most similar cluster. During the clustering process, weights are learned for each cluster. Given a series of time-stamped data up to time t, we divide it into a set of training patterns. By using the weighted self-constructing clustering, the training patterns are grouped into a set of clusters. To estimate the value at time t + 1, we find the k nearest neighbors of the input pattern and use these k neighbors to decide the estimation. Experimental results are shown to demonstrate the effectiveness of the proposed approach.
Short-Term Wave Forecasting for Real-Time Control of Wave Energy Converters
Fusco, Francesco; Ringwood, John
2010-01-01
Real-time control of wave energy converters requires knowledge of future incident wave elevation in order to approach optimal efficiency of wave energy extraction. We present an approach where the wave elevation is treated as a time series and it is predicted only from its past history. A comparison of a range of forecasting methodologies on real wave observations from two different locations shows how the relatively simple linear autoregressive model, which implicitly models the cyclical beh...
Wave Extremes in the Northeast Atlantic from Ensemble Forecasts
Breivik, Øyvind; Aarnes, Ole Johan; Bidlot, Jean-Raymond; Carrasco, Ana; Saetra, Øyvind
2013-10-01
A method for estimating return values from ensembles of forecasts at advanced lead times is presented. Return values of significant wave height in the North-East Atlantic, the Norwegian Sea and the North Sea are computed from archived +240-h forecasts of the ECMWF ensemble prediction system (EPS) from 1999 to 2009. We make three assumptions: First, each forecast is representative of a six-hour interval and collectively the data set is then comparable to a time period of 226 years. Second, the model climate matches the observed distribution, which we confirm by comparing with buoy data. Third, the ensemble members are sufficiently uncorrelated to be considered independent realizations of the model climate. We find anomaly correlations of 0.20, but peak events (>P97) are entirely uncorrelated. By comparing return values from individual members with return values of subsamples of the data set we also find that the estimates follow the same distribution and appear unaffected by correlations in the ensemble. The annual mean and variance over the 11-year archived period exhibit no significant departures from stationarity compared with a recent reforecast, i.e., there is no spurious trend due to model upgrades. EPS yields significantly higher return values than ERA-40 and ERA-Interim and is in good agreement with the high-resolution hindcast NORA10, except in the lee of unresolved islands where EPS overestimates and in enclosed seas where it is biased low. Confidence intervals are half the width of those found for ERA-Interim due to the magnitude of the data set.
The Henetus wave forecast system in the Adriatic Sea
Directory of Open Access Journals (Sweden)
L. Bertotti
2011-11-01
Full Text Available We describe the Henetus wave forecast system in the Adriatic Sea. Operational since 1996, the system is continuously upgraded, especially through the correction of the input ECMWF wind fields. As these fields are of progressively improved quality with the increasing resolution of the meteorological model, the correction needs to be correspondingly updated. This ensures a practically constant quality of the Henetus results in the Adriatic Sea since 1996. After suitable and extended validation of the quality of the results at different forecast ranges, the operational range has been recently extended to five days. The Henetus results are used also to improve the tidal forecast on the Venetian coasts and the Venice lagoon, particularly during the most severe events. Extensive statistics on the model performance are provided, both as analysis and forecast, by comparing the model results versus both satellite and buoy data.
Wave forecasting and monitoring during very severe cyclone Phailin in the Bay of Bengal.
Digital Repository Service at National Institute of Oceanography (India)
Nair, T.M.B; Remya, P.G.; Harikumar, R.; Sandhya, K.G.; Sirisha, P.; Srinivas, K.; Nagaraju, C.; Nherakkol, A.; KrishnaPrasad, B.; Jeyakumar, C.; Kaviyazhahu, K.; Hithin, N.K.; Kumari, R.; SanilKumar, V.; RameshKumar, M.; Shenoi, S.S.C.; Nayak, S.
Wave fields, both measured and forecast during the very severe cyclone Phailin, are discussed in this communication. Waves having maximum height of 13.54 m were recorded at Gopalpur, the landfall point of the cyclone. The forecast and observed...
Ensemble methods for seasonal limited area forecasts
DEFF Research Database (Denmark)
Arritt, Raymond W.; Anderson, Christopher J.; Takle, Eugene S.
2004-01-01
The ensemble prediction methods used for seasonal limited area forecasts were examined by comparing methods for generating ensemble simulations of seasonal precipitation. The summer 1993 model over the north-central US was used as a test case. The four methods examined included the lagged-average...
Surface wave effect on the upper ocean in marine forecast
Wang, Guansuo; Qiao, Fangli; Xia, Changshui; Zhao, Chang
2015-04-01
An Operational Coupled Forecast System for the seas off China and adjacent (OCFS-C) is constructed based on the paralleled wave-circulation coupled model, which is tested with comprehensive experiments and operational since November 1st, 2007. The main feature of the system is that the wave-induced mixing is considered in circulation model. Daily analyses and three day forecasts of three-dimensional temperature, salinity, currents and wave height are produced. Coverage is global at 1/2 degreed resolution with nested models up to 1/24 degree resolution in China Sea. Daily remote sensing sea surface temperatures (SST) are taken to relax to an analytical product as hot restarting fields for OCFS-C by the Nudging techniques. Forecasting-data inter-comparisons are performed to measure the effectiveness of OCFS-C in predicting upper-ocean quantities including SST, mixed layer depth (MLD) and subsurface temperature. The variety of performance with lead time and real-time is discussed as well using the daily statistic results for SST between forecast and satellite data. Several buoy observations and many Argo profiles are used for this validation. Except the conventional statistical metrics, non-dimension skill scores (SS) is taken to estimate forecast skill. Model SST comparisons with more one year-long SST time series from 2 buoys given a large SS value (more than 0.90). And skill in predicting the seasonal variability of SST is confirmed. Model subsurface temperature comparisons with that from a lot of Argo profiles indicated that OCFS-C has low skill in predicting subsurface temperatures between 80m and 120m. Inter-comparisons of MLD reveal that MLD from model is shallower than that from Argo profiles by about 12m. QCFS-C is successful and steady in predicting MLD. The daily statistic results for SST between 1-d, 2-d and 3-d forecast and data is adopted to describe variability of Skill in predicting SST with lead time or real time. In a word QCFS-C shows reasonable
Load forecasting method considering temperature effect for distribution network
Directory of Open Access Journals (Sweden)
Meng Xiao Fang
2016-01-01
Full Text Available To improve the accuracy of load forecasting, the temperature factor was introduced into the load forecasting in this paper. This paper analyzed the characteristics of power load variation, and researched the rule of the load with the temperature change. Based on the linear regression analysis, the mathematical model of load forecasting was presented with considering the temperature effect, and the steps of load forecasting were given. Used MATLAB, the temperature regression coefficient was calculated. Using the load forecasting model, the full-day load forecasting and time-sharing load forecasting were carried out. By comparing and analyzing the forecast error, the results showed that the error of time-sharing load forecasting method was small in this paper. The forecasting method is an effective method to improve the accuracy of load forecasting.
Mediterranea Forecasting System: a focus on wave-current coupling
Clementi, Emanuela; Delrosso, Damiano; Pistoia, Jenny; Drudi, Massimiliano; Fratianni, Claudia; Grandi, Alessandro; Pinardi, Nadia; Oddo, Paolo; Tonani, Marina
2016-04-01
The Mediterranean Forecasting System (MFS) is a numerical ocean prediction system that produces analyses, reanalyses and short term forecasts for the entire Mediterranean Sea and its Atlantic Ocean adjacent areas. MFS became operational in the late 90's and has been developed and continuously improved in the framework of a series of EU and National funded programs and is now part of the Copernicus Marine Service. The MFS is composed by the hydrodynamic model NEMO (Nucleus for European Modelling of the Ocean) 2-way coupled with the third generation wave model WW3 (WaveWatchIII) implemented in the Mediterranean Sea with 1/16 horizontal resolution and forced by ECMWF atmospheric fields. The model solutions are corrected by the data assimilation system (3D variational scheme adapted to the oceanic assimilation problem) with a daily assimilation cycle, using a background error correlation matrix varying seasonally and in different sub-regions of the Mediterranean Sea. The focus of this work is to present the latest modelling system upgrades and the related achieved improvements. In order to evaluate the performance of the coupled system a set of experiments has been built by coupling the wave and circulation models that hourly exchange the following fields: the sea surface currents and air-sea temperature difference are transferred from NEMO model to WW3 model modifying respectively the mean momentum transfer of waves and the wind speed stability parameter; while the neutral drag coefficient computed by WW3 model is passed to NEMO that computes the turbulent component. In order to validate the modelling system, numerical results have been compared with in-situ and remote sensing data. This work suggests that a coupled model might be capable of a better description of wave-current interactions, in particular feedback from the ocean to the waves might assess an improvement on the prediction capability of wave characteristics, while suggests to proceed toward a fully
Seasonal UK Drought Forecasting using Statistical Methods
Richardson, Doug; Fowler, Hayley; Kilsby, Chris; Serinaldi, Francesco
2016-04-01
In the UK drought is a recurrent feature of climate with potentially large impacts on public water supply. Water companies' ability to mitigate the impacts of drought by managing diminishing availability depends on forward planning and it would be extremely valuable to improve forecasts of drought on monthly to seasonal time scales. By focusing on statistical forecasting methods, this research aims to provide techniques that are simpler, faster and computationally cheaper than physically based models. In general, statistical forecasting is done by relating the variable of interest (some hydro-meteorological variable such as rainfall or streamflow, or a drought index) to one or more predictors via some formal dependence. These predictors are generally antecedent values of the response variable or external factors such as teleconnections. A candidate model is Generalised Additive Models for Location, Scale and Shape parameters (GAMLSS). GAMLSS is a very flexible class allowing for more general distribution functions (e.g. highly skewed and/or kurtotic distributions) and the modelling of not just the location parameter but also the scale and shape parameters. Additionally GAMLSS permits the forecasting of an entire distribution, allowing the output to be assessed in probabilistic terms rather than simply the mean and confidence intervals. Exploratory analysis of the relationship between long-memory processes (e.g. large-scale atmospheric circulation patterns, sea surface temperatures and soil moisture content) and drought should result in the identification of suitable predictors to be included in the forecasting model, and further our understanding of the drivers of UK drought.
Wave ensemble forecast system for tropical cyclones in the Australian region
Zieger, Stefan; Greenslade, Diana; Kepert, Jeffrey D.
2018-05-01
Forecasting of waves under extreme conditions such as tropical cyclones is vitally important for many offshore industries, but there remain many challenges. For Northwest Western Australia (NW WA), wave forecasts issued by the Australian Bureau of Meteorology have previously been limited to products from deterministic operational wave models forced by deterministic atmospheric models. The wave models are run over global (resolution 1/4∘) and regional (resolution 1/10∘) domains with forecast ranges of + 7 and + 3 day respectively. Because of this relatively coarse resolution (both in the wave models and in the forcing fields), the accuracy of these products is limited under tropical cyclone conditions. Given this limited accuracy, a new ensemble-based wave forecasting system for the NW WA region has been developed. To achieve this, a new dedicated 8-km resolution grid was nested in the global wave model. Over this grid, the wave model is forced with winds from a bias-corrected European Centre for Medium Range Weather Forecast atmospheric ensemble that comprises 51 ensemble members to take into account the uncertainties in location, intensity and structure of a tropical cyclone system. A unique technique is used to select restart files for each wave ensemble member. The system is designed to operate in real time during the cyclone season providing + 10-day forecasts. This paper will describe the wave forecast components of this system and present the verification metrics and skill for specific events.
An Overview of Short-term Statistical Forecasting Methods
DEFF Research Database (Denmark)
Elias, Russell J.; Montgomery, Douglas C.; Kulahci, Murat
2006-01-01
An overview of statistical forecasting methodology is given, focusing on techniques appropriate to short- and medium-term forecasts. Topics include basic definitions and terminology, smoothing methods, ARIMA models, regression methods, dynamic regression models, and transfer functions. Techniques...... for evaluating and monitoring forecast performance are also summarized....
An impact analysis of forecasting methods and forecasting parameters on bullwhip effect
Silitonga, R. Y. H.; Jelly, N.
2018-04-01
Bullwhip effect is an increase of variance of demand fluctuation from downstream to upstream of supply chain. Forecasting methods and forecasting parameters were recognized as some factors that affect bullwhip phenomena. To study these factors, we can develop simulations. There are several ways to simulate bullwhip effect in previous studies, such as mathematical equation modelling, information control modelling, computer program, and many more. In this study a spreadsheet program named Bullwhip Explorer was used to simulate bullwhip effect. Several scenarios were developed to show the change in bullwhip effect ratio because of the difference in forecasting methods and forecasting parameters. Forecasting methods used were mean demand, moving average, exponential smoothing, demand signalling, and minimum expected mean squared error. Forecasting parameters were moving average period, smoothing parameter, signalling factor, and safety stock factor. It showed that decreasing moving average period, increasing smoothing parameter, increasing signalling factor can create bigger bullwhip effect ratio. Meanwhile, safety stock factor had no impact to bullwhip effect.
Numerical Forecasting Experiment of the Wave Energy Resource in the China Sea
Directory of Open Access Journals (Sweden)
Chong Wei Zheng
2016-01-01
Full Text Available The short-term forecasting of wave energy is important to provide guidance for the electric power operation and power transmission system and to enhance the efficiency of energy capture and conversion. This study produced a numerical forecasting experiment of the China Sea wave energy using WAVEWATCH-III (WW3, the latest version 4.18 wave model driven by T213 (WW3-T213 and T639 (WW3-T639 wind data separately. Then the WW3-T213 and WW3-T639 were verified and compared to build a short-term wave energy forecasting structure suited for the China Sea. Considering the value of wave power density (WPD, “wave energy rose,” daily and weekly total storage and effective storage of wave energy, this study also designed a series of short-term wave energy forecasting productions. Results show that both the WW3-T213 and WW3-T639 exhibit a good skill on the numerical forecasting of the China Sea WPD, while the result of WW3-T639 is much better. Judging from WPD and daily and weekly total storage and effective storage of wave energy, great wave energy caused by cold airs was found. As there are relatively frequent cold airs in winter, early spring, and later autumn in the China Sea and the surrounding waters, abundant wave energy ensues.
Forecasting telecommunication new service demand by analogy method and combined forecast
Directory of Open Access Journals (Sweden)
Lin Feng-Jenq
2005-01-01
Full Text Available In the modeling forecast field, we are usually faced with the more difficult problems of forecasting market demand for a new service or product. A new service or product is defined as that there is absence of historical data in this new market. We hardly use models to execute the forecasting work directly. In the Taiwan telecommunication industry, after liberalization in 1996, there are many new services opened continually. For optimal investment, it is necessary that the operators, who have been granted the concessions and licenses, forecast this new service within their planning process. Though there are some methods to solve or avoid this predicament, in this paper, we will propose one forecasting procedure that integrates the concept of analogy method and the idea of combined forecast to generate new service forecast. In view of the above, the first half of this paper describes the procedure of analogy method and the approach of combined forecast, and the second half provides the case of forecasting low-tier phone demand in Taiwan to illustrate this procedure's feasibility.
Real-Time Forecasting System of Winds, Waves and Surge in Tropical Cyclones
National Research Council Canada - National Science Library
Graber, Hans C; Donelan, Mark A; Brown, Michael G; Slinn, Donald N; Hagen, Scott C; Thompson, Donald R; Jensen, Robert E; Black, Peter G; Powell, Mark D; Guiney, John L
2005-01-01
The long-term goal of this partnership is to establish an operational forecasting system of the wind field and resulting waves and surge impacting the coastline during the approach and landfall of tropical cyclones...
Real-Time Forecasting System of Winds, Waves and Surge in Tropical Cyclones
National Research Council Canada - National Science Library
Graber, Hans C; Donelan, Mark A; Brown, Michael G; Slinn, Donald N; Hagen, Scott C; Thompson, Donald R; Jensen, Robert E; Black, Peter G; Powell, Mark D; Guiney, John L
2004-01-01
The long-term goal of this partnership is to establish an operational forecasting system of the wind field and resulting waves and surge impacting the coastline during the approach and landfall of tropical cyclones...
Real-Time Forecasting System of Winds, Waves and Surge in Tropical Cyclones
National Research Council Canada - National Science Library
Graber, Hans C; Donelan, Mark A; Brown, Michael G; Slinn, Donald N; Hagen, Scott C; Thompson, Donald R; Jensen, Robert E; Black, Peter G; Powell, Mark D; Guiney, John L; Cardone, Vincent J; Cox, Andrew T; Augustus, Ellsworth H; Colonnese, Christopher P
2003-01-01
The long-term goal of this partnership is to establish an operational forecasting system of the wind field and resulting waves and surge impacting the coastline during the approach and landfall of tropical cyclones...
Short term wave forecasting, using digital filters, for improved control of Wave Energy Converters
DEFF Research Database (Denmark)
Tedd, James; Frigaard, Peter
2007-01-01
This paper presents a Digital Filter method for real time prediction of waves incident upon a Wave Energy device. The method transforms waves measured at a point ahead of the device, to expected waves incident on the device. The relationship between these incident waves and power capture is derived...... experimentally. Results are shown form measurements taken on the Wave Dragon prototype device, a floating overtopping device situated in Northern Denmark. In this case the method is able to accurately predict the surface elevation at the device 11.2 seconds before the measurement is made. This is sufficient...... to allow advanced control systems to be developed using this knowledge to significantly improve power capture....
Short term wave forecasting, using digital filters, for improved control of Wave Energy Converters
Energy Technology Data Exchange (ETDEWEB)
Tedd, J.; Frigaard, P. [Department of Civil Engineering, Aalborg University, Aalborg (Denmark)
2007-07-01
This paper presents a Digital Filter method for real time prediction of waves incident upon a Wave Energy device. The method transforms waves measured at a point ahead of the device, to expected waves incident on the device. The relationship between these incident waves and power capture is derived experimentally. Results are shown form measurements taken on the Wave Dragon prototype device, a floating overtopping device situated in Northern Denmark. In this case the method is able to accurately predict the surface elevation at the device 11.2 seconds before the measurement is made. This is sufficient to allow advanced control systems to be developed using this knowledge to significantly improve power capture.
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)
Evaluation of Operational Wave Forecasts for the Northeastern Coast of Taiwan
Directory of Open Access Journals (Sweden)
Beng-Chun Lee
2010-01-01
Full Text Available An operational regional wave forecasting system was established to fulfill the demands of maritime engineering applications on the northeastern coast of Taiwan. This Mixed system consisted of a nested SWAN numerical wave model and experienced marine meteorologists who were sent to the construction site as the in situ predictors to validate output from the numerical model so as to improve the forecasting accuracy.
Martin, A.; Ralph, F. M.; Lavers, D. A.; Kalansky, J.; Kawzenuk, B.
2015-12-01
The previous ten years has seen an explosion in research devoted to the Atmospheric River (AR) phenomena, features of the midlatitude circulation responsible for large horizontal water vapor transport. Upon landfall, ARs can be associated with 30-50% of annual precipitation in some regions, while also causing the largest flooding events in places such as coastal California. Little discussed is the role secondary frontal waves play in modulating precipitation during a landfalling AR. Secondary frontal waves develop along an existing cold front in response to baroclinic frontogenesis, often coinciding with a strong upper-tropospheric jet. If the secondary wave develops along a front associated with a landfalling AR, the resulting precipitation may be much greater or much less than originally forecasted - especially in regions where orographic uplift of horizontally transported water vapor is responsible for a large portion of precipitation. In this study, we present several cases of secondary frontal waves that have occurred in conjunction with a landfalling AR on the US West Coast. We put the impact of these cases in historical perspective using quantitative precipitation forecasts, satellite data, reanalyses, and estimates of damage related to flooding. We also discuss the dynamical mechanisms behind secondary frontal wave development and relate these mechanisms to the high spatiotemporal variability in precipitation observed during ARs with secondary frontal waves. Finally, we demonstrate that even at lead times less than 24 hours, current quantitative precipitation forecasting methods have difficulty accurately predicting the rainfall in the area near the secondary wave landfall, in some cases leading to missed or false alarm flood warnings, and suggest methods which may improve quantitative precipitation forecasts for this type of system in the future.
Water demand forecasting: review of soft computing methods.
Ghalehkhondabi, Iman; Ardjmand, Ehsan; Young, William A; Weckman, Gary R
2017-07-01
Demand forecasting plays a vital role in resource management for governments and private companies. Considering the scarcity of water and its inherent constraints, demand management and forecasting in this domain are critically important. Several soft computing techniques have been developed over the last few decades for water demand forecasting. This study focuses on soft computing methods of water consumption forecasting published between 2005 and 2015. These methods include artificial neural networks (ANNs), fuzzy and neuro-fuzzy models, support vector machines, metaheuristics, and system dynamics. Furthermore, it was discussed that while in short-term forecasting, ANNs have been superior in many cases, but it is still very difficult to pick a single method as the overall best. According to the literature, various methods and their hybrids are applied to water demand forecasting. However, it seems soft computing has a lot more to contribute to water demand forecasting. These contribution areas include, but are not limited, to various ANN architectures, unsupervised methods, deep learning, various metaheuristics, and ensemble methods. Moreover, it is found that soft computing methods are mainly used for short-term demand forecasting.
Real Time Wave Forecasting Using Wind Time History and Genetic Programming
Directory of Open Access Journals (Sweden)
A.R. Kambekar
2014-12-01
Full Text Available The significant wave height and average wave period form an essential input for operational activities in ocean and coastal areas. Such information is important in issuing appropriate warnings to people planning any construction or instillation works in the oceanic environment. Many countries over the world routinely collect wave and wind data through a network of wave rider buoys. The data collecting agencies transmit the resulting information online to their registered users through an internet or a web-based system. Operational wave forecasts in addition to the measured data are also made and supplied online to the users. This paper discusses operational wave forecasting in real time mode at locations where wind rather than wave data are continuously recorded. It is based on the time series modeling and incorporates an artificial intelligence technique of genetic programming. The significant wave height and average wave period values are forecasted over a period of 96 hr in future from the observations of wind speed and directions extending to a similar time scale in the past. Wind measurements made by floating buoys at eight different locations around India over a period varying from 1.5 yr to 9.0 yr were considered. The platform of Matlab and C++ was used to develop a graphical user interface that will extend an internet based user-friendly access of the forecasts to any registered user of the data dissemination authority.
Price forecasting of day-ahead electricity markets using a hybrid forecast method
International Nuclear Information System (INIS)
Shafie-khah, M.; Moghaddam, M. Parsa; Sheikh-El-Eslami, M.K.
2011-01-01
Research highlights: → A hybrid method is proposed to forecast the day-ahead prices in electricity market. → The method combines Wavelet-ARIMA and RBFN network models. → PSO method is applied to obtain optimum RBFN structure for avoiding over fitting. → One of the merits of the proposed method is lower need to the input data. → The proposed method has more accurate behavior in compare with previous methods. -- Abstract: Energy price forecasting in a competitive electricity market is crucial for the market participants in planning their operations and managing their risk, and it is also the key information in the economic optimization of the electric power industry. However, price series usually have a complex behavior due to their nonlinearity, nonstationarity, and time variancy. In this paper, a novel hybrid method to forecast day-ahead electricity price is proposed. This hybrid method is based on wavelet transform, Auto-Regressive Integrated Moving Average (ARIMA) models and Radial Basis Function Neural Networks (RBFN). The wavelet transform provides a set of better-behaved constitutive series than price series for prediction. ARIMA model is used to generate a linear forecast, and then RBFN is developed as a tool for nonlinear pattern recognition to correct the estimation error in wavelet-ARIMA forecast. Particle Swarm Optimization (PSO) is used to optimize the network structure which makes the RBFN be adapted to the specified training set, reducing computation complexity and avoiding overfitting. The proposed method is examined on the electricity market of mainland Spain and the results are compared with some of the most recent price forecast methods. The results show that the proposed hybrid method could provide a considerable improvement for the forecasting accuracy.
Price forecasting of day-ahead electricity markets using a hybrid forecast method
Energy Technology Data Exchange (ETDEWEB)
Shafie-khah, M., E-mail: miadreza@gmail.co [Tarbiat Modares University, Tehran (Iran, Islamic Republic of); Moghaddam, M. Parsa, E-mail: parsa@modares.ac.i [Tarbiat Modares University, Tehran (Iran, Islamic Republic of); Sheikh-El-Eslami, M.K., E-mail: aleslam@modares.ac.i [Tarbiat Modares University, Tehran (Iran, Islamic Republic of)
2011-05-15
Research highlights: {yields} A hybrid method is proposed to forecast the day-ahead prices in electricity market. {yields} The method combines Wavelet-ARIMA and RBFN network models. {yields} PSO method is applied to obtain optimum RBFN structure for avoiding over fitting. {yields} One of the merits of the proposed method is lower need to the input data. {yields} The proposed method has more accurate behavior in compare with previous methods. -- Abstract: Energy price forecasting in a competitive electricity market is crucial for the market participants in planning their operations and managing their risk, and it is also the key information in the economic optimization of the electric power industry. However, price series usually have a complex behavior due to their nonlinearity, nonstationarity, and time variancy. In this paper, a novel hybrid method to forecast day-ahead electricity price is proposed. This hybrid method is based on wavelet transform, Auto-Regressive Integrated Moving Average (ARIMA) models and Radial Basis Function Neural Networks (RBFN). The wavelet transform provides a set of better-behaved constitutive series than price series for prediction. ARIMA model is used to generate a linear forecast, and then RBFN is developed as a tool for nonlinear pattern recognition to correct the estimation error in wavelet-ARIMA forecast. Particle Swarm Optimization (PSO) is used to optimize the network structure which makes the RBFN be adapted to the specified training set, reducing computation complexity and avoiding overfitting. The proposed method is examined on the electricity market of mainland Spain and the results are compared with some of the most recent price forecast methods. The results show that the proposed hybrid method could provide a considerable improvement for the forecasting accuracy.
Extended-range forecasting of Chinese summer surface air temperature and heat waves
Zhu, Zhiwei; Li, Tim
2018-03-01
Because of growing demand from agricultural planning, power management and activity scheduling, extended-range (5-30-day lead) forecasting of summer surface air temperature (SAT) and heat waves over China is carried out in the present study via spatial-temporal projection models (STPMs). Based on the training data during 1960-1999, the predictability sources are found to propagate from Europe, Northeast Asia, and the tropical Pacific, to influence the intraseasonal 10-80 day SAT over China. STPMs are therefore constructed using the projection domains, which are determined by these previous predictability sources. For the independent forecast period (2000-2013), the STPMs can reproduce EOF-filtered 30-80 day SAT at all lead times of 5-30 days over most part of China, and observed 30-80 and 10-80 day SAT at 25-30 days over eastern China. Significant pattern correlation coefficients account for more than 50% of total forecasts at all 5-30-day lead times against EOF-filtered and observed 30-80 day SAT, and at a 20-day lead time against observed 10-80 day SAT. The STPMs perform poorly in reproducing 10-30 day SAT. Forecasting for the first two modes of 10-30 day SAT only shows useful skill within a 15-day lead time. Forecasting for the third mode of 10-30 day SAT is useless after a 10-day lead time. The forecasted heat waves over China are determined by the reconstructed SAT which is the summation of the forecasted 10-80 day SAT and the lower frequency (longer than 80-day) climatological SAT. Over a large part of China, the STPMs can forecast more than 30% of heat waves within a 15-day lead time. In general, the STPMs demonstrate the promising skill for extended-range forecasting of Chinese summer SAT and heat waves.
Wave ensemble forecast in the Western Mediterranean Sea, application to an early warning system.
Pallares, Elena; Hernandez, Hector; Moré, Jordi; Espino, Manuel; Sairouni, Abdel
2015-04-01
The Western Mediterranean Sea is a highly heterogeneous and variable area, as is reflected on the wind field, the current field, and the waves, mainly in the first kilometers offshore. As a result of this variability, the wave forecast in these regions is quite complicated to perform, usually with some accuracy problems during energetic storm events. Moreover, is in these areas where most of the economic activities take part, including fisheries, sailing, tourism, coastal management and offshore renewal energy platforms. In order to introduce an indicator of the probability of occurrence of the different sea states and give more detailed information of the forecast to the end users, an ensemble wave forecast system is considered. The ensemble prediction systems have already been used in the last decades for the meteorological forecast; to deal with the uncertainties of the initial conditions and the different parametrizations used in the models, which may introduce some errors in the forecast, a bunch of different perturbed meteorological simulations are considered as possible future scenarios and compared with the deterministic forecast. In the present work, the SWAN wave model (v41.01) has been implemented for the Western Mediterranean sea, forced with wind fields produced by the deterministic Global Forecast System (GFS) and Global Ensemble Forecast System (GEFS). The wind fields includes a deterministic forecast (also named control), between 11 and 21 ensemble members, and some intelligent member obtained from the ensemble, as the mean of all the members. Four buoys located in the study area, moored in coastal waters, have been used to validate the results. The outputs include all the time series, with a forecast horizon of 8 days and represented in spaghetti diagrams, the spread of the system and the probability at different thresholds. The main goal of this exercise is to be able to determine the degree of the uncertainty of the wave forecast, meaningful
Short-term electric load forecasting using computational intelligence methods
Jurado, Sergio; Peralta, J.; Nebot, Àngela; Mugica, Francisco; Cortez, Paulo
2013-01-01
Accurate time series forecasting is a key issue to support individual and organizational decision making. In this paper, we introduce several methods for short-term electric load forecasting. All the presented methods stem from computational intelligence techniques: Random Forest, Nonlinear Autoregressive Neural Networks, Evolutionary Support Vector Machines and Fuzzy Inductive Reasoning. The performance of the suggested methods is experimentally justified with several experiments carried out...
Method for forecasting an earthquake from precursor signals
International Nuclear Information System (INIS)
Farnworth, D.F.
1996-01-01
A method for forecasting an earthquake from precursor signals by employing characteristic first electromagnetic signals, second, seismically induced electromagnetic signals, seismically induced mechanical signals, and infrasonic acoustic signals which have been observed to precede an earthquake. From a first electromagnetic signal, a magnitude, depth beneath the surface of the earth, distance, latitude, longitude, and first and second forecasts of the time of occurrence of the impending earthquake may be derived. From a second, seismically induced electromagnetic signal and the mechanical signal, third and fourth forecasts of the time of occurrence of an impending earthquake determined from the analysis above, a magnitude, depth beneath the surface of the earth and fourth and fifth forecasts of the time of occurrence of the impending earthquake may be derived. The forecasts of time available from the above analyses range from up to five weeks to substantially within one hour in advance of the earthquake. (author)
Methods for snowmelt forecasting in upland Britain
Directory of Open Access Journals (Sweden)
R. J. Moore
1999-01-01
Full Text Available Snow, whilst not a dominant feature of Britain's maritime climate, can exert a significant influence on major floods through its contribution as snowmelt. Flood warning systems which fail to take account of melting snow can prove highly misleading. Selected results of a study on methods for improved snowmelt forecasting using trail catchments in upland Britain are presented here. Melt models considered range from a temperature excess formulation, with the option to include wind and rain heating effects, to a full energy budget melt formulation. Storage of melt in the pack is controlled by a store with two outlets, allowing slow release of water followed by rapid release once a critical liquid water content is reached. For shallow snow packs, a partial cover curve determines the proportion of the catchment over which snow extends. The melt, storage and release mechanisms together constitute the PACK snowmelt module which provides inputs to the catchment model. Either a lumped or distributed catchment model can be used, configured to receive snowmelt inputs from elevation zones within the catchment; a PACK snowmelt module operates independently within each zone and its inputs are controlled by appropriate elevation lapse rates. Measurements of snow depth and/or water equivalent, from snow cores or a snow pillow, are assimilated to correct for a lack of direct snowfall measurements needed to maintain a water balance during snowfall. The updating scheme involves operating a PACK module at the measurement site (the 'point model' in parallel to PACK modules in the catchment model, with point model errors being transferred using a proportioning scheme to adjust the snowpack water contents of the catchment model. The results of the assessment of different model variants broadly favour the simpler model formulations. Hourly automatic monitoring of water equivalent using the snow pillow can help in updating the model but preferential melting from the
Assessment of the importance of the current-wave coupling in the shelf ocean forecasts
Directory of Open Access Journals (Sweden)
G. Jordà
2007-07-01
Full Text Available The effects of wave-current interactions on shelf ocean forecasts is investigated in the framework of the MFSTEP (Mediterranean Forecasting System Project Towards Enviromental Predictions project. A one way sequential coupling approach is adopted to link the wave model (WAM to the circulation model (SYMPHONIE. The coupling of waves and currents has been done considering four main processes: wave refraction due to currents, surface wind drag and bottom drag modifications due to waves, and the wave induced mass flux. The coupled modelling system is implemented in the southern Catalan shelf (NW Mediterranean, a region with characteristics similar to most of the Mediterranean shelves. The sensitivity experiments are run in a typical operational configuration. The wave refraction by currents seems to be not very relevant in a microtidal context such as the western Mediterranean. The main effect of waves on current forecasts is through the modification of the wind drag. The Stokes drift also plays a significant role due to its spatial and temporal characteristics. Finally, the enhanced bottom friction is just noticeable in the inner shelf.
Development and testing of improved statistical wind power forecasting methods.
Energy Technology Data Exchange (ETDEWEB)
Mendes, J.; Bessa, R.J.; Keko, H.; Sumaili, J.; Miranda, V.; Ferreira, C.; Gama, J.; Botterud, A.; Zhou, Z.; Wang, J. (Decision and Information Sciences); (INESC Porto)
2011-12-06
(with spatial and/or temporal dependence). Statistical approaches to uncertainty forecasting basically consist of estimating the uncertainty based on observed forecasting errors. Quantile regression (QR) is currently a commonly used approach in uncertainty forecasting. In Chapter 3, we propose new statistical approaches to the uncertainty estimation problem by employing kernel density forecast (KDF) methods. We use two estimators in both offline and time-adaptive modes, namely, the Nadaraya-Watson (NW) and Quantilecopula (QC) estimators. We conduct detailed tests of the new approaches using QR as a benchmark. One of the major issues in wind power generation are sudden and large changes of wind power output over a short period of time, namely ramping events. In Chapter 4, we perform a comparative study of existing definitions and methodologies for ramp forecasting. We also introduce a new probabilistic method for ramp event detection. The method starts with a stochastic algorithm that generates wind power scenarios, which are passed through a high-pass filter for ramp detection and estimation of the likelihood of ramp events to happen. The report is organized as follows: Chapter 2 presents the results of the application of ITL training criteria to deterministic WPF; Chapter 3 reports the study on probabilistic WPF, including new contributions to wind power uncertainty forecasting; Chapter 4 presents a new method to predict and visualize ramp events, comparing it with state-of-the-art methodologies; Chapter 5 briefly summarizes the main findings and contributions of this report.
Forecasting space weather: Can new econometric methods improve accuracy?
Reikard, Gordon
2011-06-01
Space weather forecasts are currently used in areas ranging from navigation and communication to electric power system operations. The relevant forecast horizons can range from as little as 24 h to several days. This paper analyzes the predictability of two major space weather measures using new time series methods, many of them derived from econometrics. The data sets are the A p geomagnetic index and the solar radio flux at 10.7 cm. The methods tested include nonlinear regressions, neural networks, frequency domain algorithms, GARCH models (which utilize the residual variance), state transition models, and models that combine elements of several techniques. While combined models are complex, they can be programmed using modern statistical software. The data frequency is daily, and forecasting experiments are run over horizons ranging from 1 to 7 days. Two major conclusions stand out. First, the frequency domain method forecasts the A p index more accurately than any time domain model, including both regressions and neural networks. This finding is very robust, and holds for all forecast horizons. Combining the frequency domain method with other techniques yields a further small improvement in accuracy. Second, the neural network forecasts the solar flux more accurately than any other method, although at short horizons (2 days or less) the regression and net yield similar results. The neural net does best when it includes measures of the long-term component in the data.
Forecasting European cold waves based on subsampling strategies of CMIP5 and Euro-CORDEX ensembles
Cordero-Llana, Laura; Braconnot, Pascale; Vautard, Robert; Vrac, Mathieu; Jezequel, Aglae
2016-04-01
Forecasting future extreme events under the present changing climate represents a difficult task. Currently there are a large number of ensembles of simulations for climate projections that take in account different models and scenarios. However, there is a need for reducing the size of the ensemble to make the interpretation of these simulations more manageable for impact studies or climate risk assessment. This can be achieved by developing subsampling strategies to identify a limited number of simulations that best represent the ensemble. In this study, cold waves are chosen to test different approaches for subsampling available simulations. The definition of cold waves depends on the criteria used, but they are generally defined using a minimum temperature threshold, the duration of the cold spell as well as their geographical extend. These climate indicators are not universal, highlighting the difficulty of directly comparing different studies. As part of the of the CLIPC European project, we use daily surface temperature data obtained from CMIP5 outputs as well as Euro-CORDEX simulations to predict future cold waves events in Europe. From these simulations a clustering method is applied to minimise the number of ensembles required. Furthermore, we analyse the different uncertainties that arise from the different model characteristics and definitions of climate indicators. Finally, we will test if the same subsampling strategy can be used for different climate indicators. This will facilitate the use of the subsampling results for a wide number of impact assessment studies.
An operational wave forecasting system for the east coast of India
Sandhya, K. G.; Murty, P. L. N.; Deshmukh, Aditya N.; Balakrishnan Nair, T. M.; Shenoi, S. S. C.
2018-03-01
Demand for operational ocean state forecasting is increasing, owing to the ever-increasing marine activities in the context of blue economy. In the present study, an operational wave forecasting system for the east coast of India is proposed using unstructured Simulating WAves Nearshore model (UNSWAN). This modelling system uses very high resolution mesh near the Indian east coast and coarse resolution offshore, and thus avoids the necessity of nesting with a global wave model. The model is forced with European Centre for Medium-Range Weather Forecasts (ECMWF) winds and simulates wave parameters and wave spectra for the next 3 days. The spatial pictures of satellite data overlaid on simulated wave height show that the model is capable of simulating the significant wave heights and their gradients realistically. Spectral validation has been done using the available data to prove the reliability of the model. To further evaluate the model performance, the wave forecast for the entire year 2014 is evaluated against buoy measurements over the region at 4 waverider buoy locations. Seasonal analysis of significant wave height (Hs) at the four locations showed that the correlation between the modelled and observed was the highest (in the range 0.78-0.96) during the post-monsoon season. The variability of Hs was also the highest during this season at all locations. The error statistics showed clear seasonal and geographical location dependence. The root mean square error at Visakhapatnam was the same (0.25) for all seasons, but it was the smallest for pre-monsoon season (0.12 m and 0.17 m) for Puducherry and Gopalpur. The wind sea component showed higher variability compared to the corresponding swell component in all locations and for all seasons. The variability was picked by the model to a reasonable level in most of the cases. The results of statistical analysis show that the modelling system is suitable for use in the operational scenario.
Improving the wave forecast in the Catalan Coast
Pallares, Elena; Sanchez-Arcilla, Agustin; Espino, Manuel
2014-05-01
This study has been motivated by the limited accuracy of wave models under short-duration, fetch-limited conditions. This applies particularly to the wave period, and can be illustrated by the case of semi-enclosed domains with highly variable wind patterns such as the Catalan coast in the Spanish Mediterranean. The wave model SWAN version 40.91A is used here in three nested grids covering all the North-western Mediterranean Sea with resolution from 9 to 1 km, forced with high resolution wind patterns from BSC (Barcelona Supercomputing Center) for two study periods, the winter 2010 and the spring 2011. The results are validated in eight locations with different types of instrumentation. In order to improve the results, a modification of the whitecapping well-known formulation of Hasselmann (1974) has been considered. The delta coefficient is increased to adapt the dissipation to the growth rates actually observed in the region. This correction introduces a dependence on the squared wave number, improving the prediction of the energy spectra at lower frequencies. However, one may note that an over-prediction will occur for waves with longer fetch and/or duration. The results obtained show a clear improvement of the mean and peak wave periods for the study area, decreasing considerably the negative bias observed previously, while almost no change is observed in wave height due to the proposed modifications. These results can be generalized to the Spanish Mediterranean coast and could be exported to similar environments, characterized by young/moderate sea wave conditions due to limited fetch and transient wind driving. References: - Hasselmann, K., 1974. On the spectral dissipation of ocean waves due to whitecapping. Boundary-layer Meteorology,6,107-127.
A Novel Grey Wave Method for Predicting Total Chinese Trade Volume
Directory of Open Access Journals (Sweden)
Kedong Yin
2017-12-01
Full Text Available The total trade volume of a country is an important way of appraising its international trade situation. A prediction based on trade volume will help enterprises arrange production efficiently and promote the sustainability of the international trade. Because the total Chinese trade volume fluctuates over time, this paper proposes a Grey wave forecasting model with a Hodrick–Prescott filter (HP filter to forecast it. This novel model first parses time series into long-term trend and short-term cycle. Second, the model uses a general GM (1,1 to predict the trend term and the Grey wave forecasting model to predict the cycle term. Empirical analysis shows that the improved Grey wave prediction method provides a much more accurate forecast than the basic Grey wave prediction method, achieving better prediction results than autoregressive moving average model (ARMA.
Directory of Open Access Journals (Sweden)
R. Inghilesi
2012-02-01
Full Text Available A coastal forecasting system was implemented to provide wind wave forecasts over the whole Mediterranean Sea area, and with the added capability to focus on selected coastal areas. The goal of the system was to achieve a representation of the small-scale coastal processes influencing the propagation of waves towards the coasts. The system was based on a chain of nested wave models and adopted the WAve Model (WAM to analyse the large-scale, deep-sea propagation of waves; and the Simulating WAves Nearshore (SWAN to simulate waves in key coastal areas. Regional intermediate-scale WAM grids were introduced to bridge the gap between the large-scale and each coastal area. Even applying two consecutive nestings (Mediterranean grid → regional grid → coastal grid, a very high resolution was still required for the large scale WAM implementation in order to get a final resolution of about 400 m on the shores. In this study three regional areas in the Tyrrhenian Sea were selected, with a single coastal area embedded in each of them. The number of regional and coastal grids in the system could easily be modified without significantly affecting the efficiency of the system. The coastal system was tested in three Italian coastal regions in order to optimize the numerical parameters and to check the results in orographically complex zones for which wave records were available. Fifteen storm events in the period 2004–2009 were considered.
An operational coupled wave-current forecasting system for the northern Adriatic Sea
Russo, A.; Coluccelli, A.; Deserti, M.; Valentini, A.; Benetazzo, A.; Carniel, S.
2012-04-01
Since 2005 an Adriatic implementation of the Regional Ocean Modeling System (AdriaROMS) is being producing operational short-term forecasts (72 hours) of some hydrodynamic properties (currents, sea level, temperature, salinity) of the Adriatic Sea at 2 km horizontal resolution and 20 vertical s-levels, on a daily basis. The main objective of AdriaROMS, which is managed by the Hydro-Meteo-Clima Service (SIMC) of ARPA Emilia Romagna, is to provide useful products for civil protection purposes (sea level forecasts, outputs to run other forecasting models as for saline wedge, oil spills and coastal erosion). In order to improve the forecasts in the coastal area, where most of the attention is focused, a higher resolution model (0.5 km, again with 20 vertical s-levels) has been implemented for the northern Adriatic domain. The new implementation is based on the Coupled-Ocean-Atmosphere-Wave-Sediment Transport Modeling System (COAWST)and adopts ROMS for the hydrodynamic and Simulating WAve Nearshore (SWAN) for the wave module, respectively. Air-sea fluxes are computed using forecasts produced by the COSMO-I7 operational atmospheric model. At the open boundary of the high resolution model, temperature, salinity and velocity fields are provided by AdriaROMS while the wave characteristics are provided by an operational SWAN implementation (also managed by SIMC). Main tidal components are imposed as well, derived from a tidal model. Work in progress is oriented now on the validation of model results by means of extensive comparisons with acquired hydrographic measurements (such as CTDs or XBTs from sea-truth campaigns), currents and waves acquired at observational sites (including those of SIMC, CNR-ISMAR network and its oceanographic tower, located off the Venice littoral) and satellite-derived wave-heights data. Preliminary results on the forecast waves denote how, especially during intense storms, the effect of coupling can lead to significant variations in the wave
A Comparison of Various Forecasting Methods for Autocorrelated Time Series
Directory of Open Access Journals (Sweden)
Karin Kandananond
2012-07-01
Full Text Available The accuracy of forecasts significantly affects the overall performance of a whole supply chain system. Sometimes, the nature of consumer products might cause difficulties in forecasting for the future demands because of its complicated structure. In this study, two machine learning methods, artificial neural network (ANN and support vector machine (SVM, and a traditional approach, the autoregressive integrated moving average (ARIMA model, were utilized to predict the demand for consumer products. The training data used were the actual demand of six different products from a consumer product company in Thailand. Initially, each set of data was analysed using Ljung‐Box‐Q statistics to test for autocorrelation. Afterwards, each method was applied to different sets of data. The results indicated that the SVM method had a better forecast quality (in terms of MAPE than ANN and ARIMA in every category of products.
Evaluating Downscaling Methods for Seasonal Climate Forecasts over East Africa
Roberts, J. Brent; Robertson, Franklin R.; Bosilovich, Michael; Lyon, Bradfield; Funk, Chris
2013-01-01
The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in impact modeling within hub regions including East Africa, the Hindu Kush-Himalayan (HKH) region and Mesoamerica. One of the participating models in NMME is the NASA Goddard Earth Observing System (GEOS5). This work will present an intercomparison of downscaling methods using the GEOS5 seasonal forecasts of temperature and precipitation over East Africa. The current seasonal forecasting system provides monthly averaged forecast anomalies. These anomalies must be spatially downscaled and temporally disaggregated for use in application modeling (e.g. hydrology, agriculture). There are several available downscaling methodologies that can be implemented to accomplish this goal. Selected methods include both a non-homogenous hidden Markov model and an analogue based approach. A particular emphasis will be placed on quantifying the ability of different methods to capture the intermittency of precipitation within both the short and long rain seasons. Further, the ability to capture spatial covariances will be assessed. Both probabilistic and deterministic skill measures will be evaluated over the hindcast period
Two quantitative forecasting methods for macroeconomic indicators in Czech Republic
Directory of Open Access Journals (Sweden)
Mihaela BRATU (SIMIONESCU
2012-03-01
Full Text Available Econometric modelling and exponential smoothing techniques are two quantitative forecasting methods with good results in practice, but the objective of the research was to find out which of the two techniques are better for short run predictions. Therefore, for inflation, unemployment and interest rate in Czech Republic some accuracy indicators were calculated for the predictions based on these methods. Short run forecasts on a horizon of 3 months were made for December 2011-February 2012, the econometric models being updated. For Czech Republic, the exponential smoothing techniques provided more accurate forecasts than the econometric models (VAR(2 models, ARMA procedure and models with lagged variables. One explication for the better performance of smoothing techniques would be that in the chosen countries the short run predictions more influenced by the recent evolution of the indicators.
Multivariate methods and forecasting with IBM SPSS statistics
Aljandali, Abdulkader
2017-01-01
This is the second of a two-part guide to quantitative analysis using the IBM SPSS Statistics software package; this volume focuses on multivariate statistical methods and advanced forecasting techniques. More often than not, regression models involve more than one independent variable. For example, forecasting methods are commonly applied to aggregates such as inflation rates, unemployment, exchange rates, etc., that have complex relationships with determining variables. This book introduces multivariate regression models and provides examples to help understand theory underpinning the model. The book presents the fundamentals of multivariate regression and then moves on to examine several related techniques that have application in business-orientated fields such as logistic and multinomial regression. Forecasting tools such as the Box-Jenkins approach to time series modeling are introduced, as well as exponential smoothing and naïve techniques. This part also covers hot topics such as Factor Analysis, Dis...
Assessing methods for developing crop forecasting in the Iberian Peninsula
Ines, A. V. M.; Capa Morocho, M. I.; Baethgen, W.; Rodriguez-Fonseca, B.; Han, E.; Ruiz Ramos, M.
2015-12-01
Seasonal climate prediction may allow predicting crop yield to reduce the vulnerability of agricultural production to climate variability and its extremes. It has been already demonstrated that seasonal climate predictions at European (or Iberian) scale from ensembles of global coupled climate models have some skill (Palmer et al., 2004). The limited predictability that exhibits the atmosphere in mid-latitudes, and therefore de Iberian Peninsula (PI), can be managed by a probabilistic approach based in terciles. This study presents an application for the IP of two methods for linking tercile-based seasonal climate forecasts with crop models to improve crop predictability. Two methods were evaluated and applied for disaggregating seasonal rainfall forecasts into daily weather realizations: 1) a stochastic weather generator and 2) a forecast tercile resampler. Both methods were evaluated in a case study where the impacts of two seasonal rainfall forecasts (wet and dry forecast for 1998 and 2015 respectively) on rainfed wheat yield and irrigation requirements of maize in IP were analyzed. Simulated wheat yield and irrigation requirements of maize were computed with the crop models CERES-wheat and CERES-maize which are included in Decision Support System for Agrotechnology Transfer (DSSAT v.4.5, Hoogenboom et al., 2010). Simulations were run at several locations in Spain where the crop model was calibrated and validated with independent field data. These methodologies would allow quantifying the benefits and risks of a seasonal climate forecast to potential users as farmers, agroindustry and insurance companies in the IP. Therefore, we would be able to establish early warning systems and to design crop management adaptation strategies that take advantage of favorable conditions or reduce the effect of adverse ones. ReferencesPalmer, T. et al., 2004. Development of a European multimodel ensemble system for seasonal-to-interannual prediction (DEMETER). Bulletin of the
Statistical and Machine Learning forecasting methods: Concerns and ways forward.
Makridakis, Spyros; Spiliotis, Evangelos; Assimakopoulos, Vassilios
2018-01-01
Machine Learning (ML) methods have been proposed in the academic literature as alternatives to statistical ones for time series forecasting. Yet, scant evidence is available about their relative performance in terms of accuracy and computational requirements. The purpose of this paper is to evaluate such performance across multiple forecasting horizons using a large subset of 1045 monthly time series used in the M3 Competition. After comparing the post-sample accuracy of popular ML methods with that of eight traditional statistical ones, we found that the former are dominated across both accuracy measures used and for all forecasting horizons examined. Moreover, we observed that their computational requirements are considerably greater than those of statistical methods. The paper discusses the results, explains why the accuracy of ML models is below that of statistical ones and proposes some possible ways forward. The empirical results found in our research stress the need for objective and unbiased ways to test the performance of forecasting methods that can be achieved through sizable and open competitions allowing meaningful comparisons and definite conclusions.
Statistical and Machine Learning forecasting methods: Concerns and ways forward
Makridakis, Spyros; Assimakopoulos, Vassilios
2018-01-01
Machine Learning (ML) methods have been proposed in the academic literature as alternatives to statistical ones for time series forecasting. Yet, scant evidence is available about their relative performance in terms of accuracy and computational requirements. The purpose of this paper is to evaluate such performance across multiple forecasting horizons using a large subset of 1045 monthly time series used in the M3 Competition. After comparing the post-sample accuracy of popular ML methods with that of eight traditional statistical ones, we found that the former are dominated across both accuracy measures used and for all forecasting horizons examined. Moreover, we observed that their computational requirements are considerably greater than those of statistical methods. The paper discusses the results, explains why the accuracy of ML models is below that of statistical ones and proposes some possible ways forward. The empirical results found in our research stress the need for objective and unbiased ways to test the performance of forecasting methods that can be achieved through sizable and open competitions allowing meaningful comparisons and definite conclusions. PMID:29584784
The international workshop on wave hindcasting and forecasting and the coastal hazards symposium
Breivik, Øyvind; Swail, Val; Babanin, Alexander V.; Horsburgh, Kevin
2015-05-01
Following the 13th International Workshop on Wave Hindcasting and Forecasting and 4th Coastal Hazards Symposium in October 2013 in Banff, Canada, a topical collection has appeared in recent issues of Ocean Dynamics. Here, we give a brief overview of the history of the conference since its inception in 1986 and of the progress made in the fields of wind-generated ocean waves and the modelling of coastal hazards before we summarize the main results of the papers that have appeared in the topical collection.
A robust method to forecast volcanic ash clouds
Denlinger, Roger P.; Pavolonis, Mike; Sieglaff, Justin
2012-01-01
Ash clouds emanating from volcanic eruption columns often form trails of ash extending thousands of kilometers through the Earth's atmosphere, disrupting air traffic and posing a significant hazard to air travel. To mitigate such hazards, the community charged with reducing flight risk must accurately assess risk of ash ingestion for any flight path and provide robust forecasts of volcanic ash dispersal. In response to this need, a number of different transport models have been developed for this purpose and applied to recent eruptions, providing a means to assess uncertainty in forecasts. Here we provide a framework for optimal forecasts and their uncertainties given any model and any observational data. This involves random sampling of the probability distributions of input (source) parameters to a transport model and iteratively running the model with different inputs, each time assessing the predictions that the model makes about ash dispersal by direct comparison with satellite data. The results of these comparisons are embodied in a likelihood function whose maximum corresponds to the minimum misfit between model output and observations. Bayes theorem is then used to determine a normalized posterior probability distribution and from that a forecast of future uncertainty in ash dispersal. The nature of ash clouds in heterogeneous wind fields creates a strong maximum likelihood estimate in which most of the probability is localized to narrow ranges of model source parameters. This property is used here to accelerate probability assessment, producing a method to rapidly generate a prediction of future ash concentrations and their distribution based upon assimilation of satellite data as well as model and data uncertainties. Applying this method to the recent eruption of Eyjafjallajökull in Iceland, we show that the 3 and 6 h forecasts of ash cloud location probability encompassed the location of observed satellite-determined ash cloud loads, providing an
Energy demand forecasting method based on international statistical data
International Nuclear Information System (INIS)
Glanc, Z.; Kerner, A.
1997-01-01
Poland is in a transition phase from a centrally planned to a market economy; data collected under former economic conditions do not reflect a market economy. Final energy demand forecasts are based on the assumption that the economic transformation in Poland will gradually lead the Polish economy, technologies and modes of energy use, to the same conditions as mature market economy countries. The starting point has a significant influence on the future energy demand and supply structure: final energy consumption per capita in 1992 was almost half the average of OECD countries; energy intensity, based on Purchasing Power Parities (PPP) and referred to GDP, is more than 3 times higher in Poland. A method of final energy demand forecasting based on regression analysis is described in this paper. The input data are: output of macroeconomic and population growth forecast; time series 1970-1992 of OECD countries concerning both macroeconomic characteristics and energy consumption; and energy balance of Poland for the base year of the forecast horizon. (author). 1 ref., 19 figs, 4 tabs
Energy demand forecasting method based on international statistical data
Energy Technology Data Exchange (ETDEWEB)
Glanc, Z; Kerner, A [Energy Information Centre, Warsaw (Poland)
1997-09-01
Poland is in a transition phase from a centrally planned to a market economy; data collected under former economic conditions do not reflect a market economy. Final energy demand forecasts are based on the assumption that the economic transformation in Poland will gradually lead the Polish economy, technologies and modes of energy use, to the same conditions as mature market economy countries. The starting point has a significant influence on the future energy demand and supply structure: final energy consumption per capita in 1992 was almost half the average of OECD countries; energy intensity, based on Purchasing Power Parities (PPP) and referred to GDP, is more than 3 times higher in Poland. A method of final energy demand forecasting based on regression analysis is described in this paper. The input data are: output of macroeconomic and population growth forecast; time series 1970-1992 of OECD countries concerning both macroeconomic characteristics and energy consumption; and energy balance of Poland for the base year of the forecast horizon. (author). 1 ref., 19 figs, 4 tabs.
Benefits of up-wave measurements in linear short-term wave forecasting for wave energy applications
Paparella, Francesco; Monk, Kieran; Winands, Victor; Lopes, Miguel; Conley, Daniel; Ringwood, John
2014-01-01
The real-time control of wave energy converters requires the prediction of the wave elevation at the location of the device in order to maximize the power extracted from the waves. One possibility is to predict the future wave elevation by combining its past history with the spatial information coming from a sensor which measures the free surface elevation upwave of the wave energy converter. As an application example, the paper focuses on the prediction of the wave eleva...
Developing energy forecasting model using hybrid artificial intelligence method
Institute of Scientific and Technical Information of China (English)
Shahram Mollaiy-Berneti
2015-01-01
An important problem in demand planning for energy consumption is developing an accurate energy forecasting model. In fact, it is not possible to allocate the energy resources in an optimal manner without having accurate demand value. A new energy forecasting model was proposed based on the back-propagation (BP) type neural network and imperialist competitive algorithm. The proposed method offers the advantage of local search ability of BP technique and global search ability of imperialist competitive algorithm. Two types of empirical data regarding the energy demand (gross domestic product (GDP), population, import, export and energy demand) in Turkey from 1979 to 2005 and electricity demand (population, GDP, total revenue from exporting industrial products and electricity consumption) in Thailand from 1986 to 2010 were investigated to demonstrate the applicability and merits of the present method. The performance of the proposed model is found to be better than that of conventional back-propagation neural network with low mean absolute error.
Housing Value Forecasting Based on Machine Learning Methods
Mu, Jingyi; Wu, Fang; Zhang, Aihua
2014-01-01
In the era of big data, many urgent issues to tackle in all walks of life all can be solved via big data technique. Compared with the Internet, economy, industry, and aerospace fields, the application of big data in the area of architecture is relatively few. In this paper, on the basis of the actual data, the values of Boston suburb houses are forecast by several machine learning methods. According to the predictions, the government and developers can make decisions about whether developing...
Housing Value Forecasting Based on Machine Learning Methods
Directory of Open Access Journals (Sweden)
Jingyi Mu
2014-01-01
Full Text Available In the era of big data, many urgent issues to tackle in all walks of life all can be solved via big data technique. Compared with the Internet, economy, industry, and aerospace fields, the application of big data in the area of architecture is relatively few. In this paper, on the basis of the actual data, the values of Boston suburb houses are forecast by several machine learning methods. According to the predictions, the government and developers can make decisions about whether developing the real estate on corresponding regions or not. In this paper, support vector machine (SVM, least squares support vector machine (LSSVM, and partial least squares (PLS methods are used to forecast the home values. And these algorithms are compared according to the predicted results. Experiment shows that although the data set exists serious nonlinearity, the experiment result also show SVM and LSSVM methods are superior to PLS on dealing with the problem of nonlinearity. The global optimal solution can be found and best forecasting effect can be achieved by SVM because of solving a quadratic programming problem. In this paper, the different computation efficiencies of the algorithms are compared according to the computing times of relevant algorithms.
DEVELOPMENT OF THE PROBABLY-GEOGRAPHICAL FORECAST METHOD FOR DANGEROUS WEATHER PHENOMENA
Directory of Open Access Journals (Sweden)
Elena S. Popova
2015-12-01
Full Text Available This paper presents a scheme method of probably-geographical forecast for dangerous weather phenomena. Discuss two general realization stages of this method. Emphasize that developing method is response to actual questions of modern weather forecast and it’s appropriate phenomena: forecast is carried out for specific point in space and appropriate moment of time.
Frequency domain methods applied to forecasting electricity markets
International Nuclear Information System (INIS)
Trapero, Juan R.; Pedregal, Diego J.
2009-01-01
The changes taking place in electricity markets during the last two decades have produced an increased interest in the problem of forecasting, either load demand or prices. Many forecasting methodologies are available in the literature nowadays with mixed conclusions about which method is most convenient. This paper focuses on the modeling of electricity market time series sampled hourly in order to produce short-term (1 to 24 h ahead) forecasts. The main features of the system are that (1) models are of an Unobserved Component class that allow for signal extraction of trend, diurnal, weekly and irregular components; (2) its application is automatic, in the sense that there is no need for human intervention via any sort of identification stage; (3) the models are estimated in the frequency domain; and (4) the robustness of the method makes possible its direct use on both load demand and price time series. The approach is thoroughly tested on the PJM interconnection market and the results improve on classical ARIMA models. (author)
The Variance-covariance Method using IOWGA Operator for Tourism Forecast Combination
Directory of Open Access Journals (Sweden)
Liangping Wu
2014-08-01
Full Text Available Three combination methods commonly used in tourism forecasting are the simple average method, the variance-covariance method and the discounted MSFE method. These methods assign the different weights that can not change at each time point to each individual forecasting model. In this study, we introduce the IOWGA operator combination method which can overcome the defect of previous three combination methods into tourism forecasting. Moreover, we further investigate the performance of the four combination methods through the theoretical evaluation and the forecasting evaluation. The results of the theoretical evaluation show that the IOWGA operator combination method obtains extremely well performance and outperforms the other forecast combination methods. Furthermore, the IOWGA operator combination method can be of well forecast performance and performs almost the same to the variance-covariance combination method for the forecasting evaluation. The IOWGA operator combination method mainly reflects the maximization of improving forecasting accuracy and the variance-covariance combination method mainly reflects the decrease of the forecast error. For future research, it may be worthwhile introducing and examining other new combination methods that may improve forecasting accuracy or employing other techniques to control the time for updating the weights in combined forecasts.
Pallares, Elena; Espino, Manuel; Sánchez-Arcilla, Agustín
2013-04-01
The Catalan Coast is located in the North Western Mediterranean Sea. It is a region with highly heterogeneous wind and wave conditions, characterized by a microtidal environment, and economically very dependent from the sea and the coastal zone activities. Because some of the main coastal conflicts and management problems occur within a few kilometers of the land-ocean boundary, the level of resolution and accuracy from meteo-oceanographic predictions required is not currently available. The current work is focused on improving high resolution wave forecasting very near the coast. The SWAN wave model is used to simulate the waves in the area, and various buoy data and field campaigns are used to validate the results. The simulations are structured in four different domains covering all the North Western Mediterranean Sea, with a grid resolution from 9 km to 250 meters in coastal areas. Previous results show that the significant wave height is almost always underpredicted in this area, and the underprediction is higher during storm events. However, the error in the peak period and the mean period is almost always constantly under predicted with a bias between one and two seconds, plus some residual error. This systematic error represents 40% of the total error. To improve the initial results, the whiteccaping dissipation term is studied and modified. In the SWAN model, the whitecapping is mainly controlled by the steepness of the waves. Although the by default parameter is not depending on the wave number, there is a new formulation in the last SWAN version (40.81) to include it in the calculations. Previous investigations show that adjusting the dependence for the wave number improved the predictions for the wave energy at lower frequencies, solving the underprediction of the period mentioned before. In the present work different simulations are developed to calibrate the new formulation, obtaining important improvements in the results. For the significant wave
Modeling North Atlantic Nor'easters With Modern Wave Forecast Models
Perrie, Will; Toulany, Bechara; Roland, Aron; Dutour-Sikiric, Mathieu; Chen, Changsheng; Beardsley, Robert C.; Qi, Jianhua; Hu, Yongcun; Casey, Michael P.; Shen, Hui
2018-01-01
Three state-of-the-art operational wave forecast model systems are implemented on fine-resolution grids for the Northwest Atlantic. These models are: (1) a composite model system consisting of SWAN implemented within WAVEWATCHIII® (the latter is hereafter, WW3) on a nested system of traditional structured grids, (2) an unstructured grid finite-volume wave model denoted "SWAVE," using SWAN physics, and (3) an unstructured grid finite element wind wave model denoted as "WWM" (for "wind wave model") which uses WW3 physics. Models are implemented on grid systems that include relatively large domains to capture the wave energy generated by the storms, as well as including fine-resolution nearshore regions of the southern Gulf of Maine with resolution on the scale of 25 m to simulate areas where inundation and coastal damage have occurred, due to the storms. Storm cases include three intense midlatitude cases: a spring Nor'easter storm in May 2005, the Patriot's Day storm in 2007, and the Boxing Day storm in 2010. Although these wave model systems have comparable overall properties in terms of their performance and skill, it is found that there are differences. Models that use more advanced physics, as presented in recent versions of WW3, tuned to regional characteristics, as in the Gulf of Maine and the Northwest Atlantic, can give enhanced results.
Gabor Wave Packet Method to Solve Plasma Wave Equations
International Nuclear Information System (INIS)
Pletzer, A.; Phillips, C.K.; Smithe, D.N.
2003-01-01
A numerical method for solving plasma wave equations arising in the context of mode conversion between the fast magnetosonic and the slow (e.g ion Bernstein) wave is presented. The numerical algorithm relies on the expansion of the solution in Gaussian wave packets known as Gabor functions, which have good resolution properties in both real and Fourier space. The wave packets are ideally suited to capture both the large and small wavelength features that characterize mode conversion problems. The accuracy of the scheme is compared with a standard finite element approach
FORECASTING PILE SETTLEMENT ON CLAYSTONE USING NUMERICAL AND ANALYTICAL METHODS
Directory of Open Access Journals (Sweden)
Ponomarev Andrey Budimirovich
2016-06-01
Full Text Available In the article the problem of designing pile foundations on claystones is reviewed. The purpose of this paper is comparative analysis of the analytical and numerical methods for forecasting the settlement of piles on claystones. The following tasks were solved during the study: 1 The existing researches of pile settlement are analyzed; 2 The characteristics of experimental studies and the parameters for numerical modeling are presented, methods of field research of single piles’ operation are described; 3 Calculation of single pile settlement is performed using numerical methods in the software package Plaxis 2D and analytical method according to the requirements SP 24.13330.2011; 4 Experimental data is compared with the results of analytical and numerical calculations; 5 Basing on these results recommendations for forecasting pile settlement on claystone are presented. Much attention is paid to the calculation of pile settlement considering the impacted areas in ground space beside pile and the comparison with the results of field experiments. Basing on the obtained results, for the prediction of settlement of single pile on claystone the authors recommend using the analytical method considered in SP 24.13330.2011 with account for the impacted areas in ground space beside driven pile. In the case of forecasting the settlement of single pile on claystone by numerical methods in Plaxis 2D the authors recommend using the Hardening Soil model considering the impacted areas in ground space beside the driven pile. The analyses of the results and calculations are presented for examination and verification; therefore it is necessary to continue the research work of deep foundation at another experimental sites to improve the reliability of the calculation of pile foundation settlement. The work is of great interest for geotechnical engineers engaged in research, design and construction of pile foundations.
Analysis of forecasting methods of cargo flows in International transportation by land transport
Ponomareva, N.
2005-01-01
Advantages and disadvantages of the existing forecasting methods of cargo flows are presented. The improvement of cargo flows forecasting method in international transportation by land transport is considered on the basis of a interregional balance model to get more correct and fuller forecast.
A method for short term electricity spot price forecasting
International Nuclear Information System (INIS)
Koreneff, G.; Seppaelae, A.; Lehtonen, M.; Kekkonen, V.; Laitinen, E.; Haekli, J.; Antila, E.
1998-01-01
In Finland, the electricity market was de-regulated in November 1995. For the electricity purchase of power companies this has caused big changes, since the old tariff based contracts of bulk power supply have been replaced by negotiated bilateral short term contracts and by power purchase from the spot market. In the spot market, in turn, there are at the present two strong actors: The electricity exchange of Finland and the Nordic power pool which is run by the Swedish and Norwegian companies. Today, the power companies in Finland have short term trade with both of the electricity exchanges. The aim of this chapter is to present methods for spot price forecasting in the electricity exchange. The main focus is given to the Finnish circumstances. In the beginning of the presentation, the practices of the electricity exchange of Finland are described, and a brief presentation is given on the different contracts, or electricity products, available in the spot market. For comparison, the practices of the Nordic electricity exchange are also outlined. A time series technique for spot price forecasting is presented. The structure of the model is presented, and its validity is tested using real case data obtained from the Finnish power market. The spot price forecasting model is a part of a computer system for distribution energy management (DEM) in a de-regulated power market
A method for short term electricity spot price forecasting
Energy Technology Data Exchange (ETDEWEB)
Koreneff, G; Seppaelae, A; Lehtonen, M; Kekkonen, V [VTT Energy, Espoo (Finland); Laitinen, E; Haekli, J [Vaasa Univ. (Finland); Antila, E [ABB Transmit Oy (Finland)
1998-08-01
In Finland, the electricity market was de-regulated in November 1995. For the electricity purchase of power companies this has caused big changes, since the old tariff based contracts of bulk power supply have been replaced by negotiated bilateral short term contracts and by power purchase from the spot market. In the spot market, in turn, there are at the present two strong actors: The electricity exchange of Finland and the Nordic power pool which is run by the Swedish and Norwegian companies. Today, the power companies in Finland have short term trade with both of the electricity exchanges. The aim of this chapter is to present methods for spot price forecasting in the electricity exchange. The main focus is given to the Finnish circumstances. In the beginning of the presentation, the practices of the electricity exchange of Finland are described, and a brief presentation is given on the different contracts, or electricity products, available in the spot market. For comparison, the practices of the Nordic electricity exchange are also outlined. A time series technique for spot price forecasting is presented. The structure of the model is presented, and its validity is tested using real case data obtained from the Finnish power market. The spot price forecasting model is a part of a computer system for distribution energy management (DEM) in a de-regulated power market
A multiscale forecasting method for power plant fleet management
Chen, Hongmei
In recent years the electric power industry has been challenged by a high level of uncertainty and volatility brought on by deregulation and globalization. A power producer must minimize the life cycle cost while meeting stringent safety and regulatory requirements and fulfilling customer demand for high reliability. Therefore, to achieve true system excellence, a more sophisticated system-level decision-making process with a more accurate forecasting support system to manage diverse and often widely dispersed generation units as a single, easily scaled and deployed fleet system in order to fully utilize the critical assets of a power producer has been created as a response. The process takes into account the time horizon for each of the major decision actions taken in a power plant and develops methods for information sharing between them. These decisions are highly interrelated and no optimal operation can be achieved without sharing information in the overall process. The process includes a forecasting system to provide information for planning for uncertainty. A new forecasting method is proposed, which utilizes a synergy of several modeling techniques properly combined at different time-scales of the forecasting objects. It can not only take advantages of the abundant historical data but also take into account the impact of pertinent driving forces from the external business environment to achieve more accurate forecasting results. Then block bootstrap is utilized to measure the bias in the estimate of the expected life cycle cost which will actually be needed to drive the business for a power plant in the long run. Finally, scenario analysis is used to provide a composite picture of future developments for decision making or strategic planning. The decision-making process is applied to a typical power producer chosen to represent challenging customer demand during high-demand periods. The process enhances system excellence by providing more accurate market
Mishra, Abhilash; Hirata, Christopher M.
2018-05-01
In the first paper of this series, we showed that the CMB quadrupole at high redshifts results in a small circular polarization of the emitted 21 cm radiation. In this paper we forecast the sensitivity of future radio experiments to measure the CMB quadrupole during the era of first cosmic light (z ˜20 ). The tomographic measurement of 21 cm circular polarization allows us to construct a 3D remote quadrupole field. Measuring the B -mode component of this remote quadrupole field can be used to put bounds on the tensor-to-scalar ratio r . We make Fisher forecasts for a future Fast Fourier Transform Telescope (FFTT), consisting of an array of dipole antennas in a compact grid configuration, as a function of array size and observation time. We find that a FFTT with a side length of 100 km can achieve σ (r )˜4 ×10-3 after ten years of observation and with a sky coverage fsky˜0.7 . The forecasts are dependent on the evolution of the Lyman-α flux in the pre-reionization era, that remains observationally unconstrained. Finally, we calculate the typical order of magnitudes for circular polarization foregrounds and comment on their mitigation strategies. We conclude that detection of primordial gravitational waves with 21 cm observations is in principle possible, so long as the primordial magnetic field amplitude is small, but would require a very futuristic experiment with corresponding advances in calibration and foreground suppression techniques.
Comparison between ARIMA and DES Methods of Forecasting Population for Housing Demand in Johor
Alias Ahmad Rizal; Zainun Noor Yasmin; Abdul Rahman Ismail
2016-01-01
Forecasting accuracy is a primary criterion in selecting appropriate method of prediction. Even though there are various methods of forecasting however not all of these methods are able to predict with good accuracy. This paper presents an evaluation of two methods of population forecasting for housing demand. These methods are Autoregressive Integrated Moving Average (ARIMA) and Double Exponential Smoothing (DES). Both of the methods are principally adopting univariate time series analysis w...
Conservative numerical methods for solitary wave interactions
Energy Technology Data Exchange (ETDEWEB)
Duran, A; Lopez-Marcos, M A [Departamento de Matematica Aplicada y Computacion, Facultad de Ciencias, Universidad de Valladolid, Paseo del Prado de la Magdalena s/n, 47005 Valladolid (Spain)
2003-07-18
The purpose of this paper is to show the advantages that represent the use of numerical methods that preserve invariant quantities in the study of solitary wave interactions for the regularized long wave equation. It is shown that the so-called conservative methods are more appropriate to study the phenomenon and provide a dynamic point of view that allows us to estimate the changes in the parameters of the solitary waves after the collision.
Rough Precipitation Forecasts based on Analogue Method: an Operational System
Raffa, Mario; Mercogliano, Paola; Lacressonnière, Gwendoline; Guillaume, Bruno; Deandreis, Céline; Castanier, Pierre
2017-04-01
In the framework of the Climate KIC partnership, has been funded the project Wat-Ener-Cast (WEC), coordinated by ARIA Technologies, having the goal to adapt, through tailored weather-related forecast, the water and energy operations to the increased weather fluctuation and to climate change. The WEC products allow providing high quality forecast suited in risk and opportunities assessment dashboard for water and energy operational decisions and addressing the needs of sewage/water distribution operators, energy transport & distribution system operators, energy manager and wind energy producers. A common "energy water" web platform, able to interface with newest smart water-energy IT network have been developed. The main benefit by sharing resources through the "WEC platform" is the possibility to optimize the cost and the procedures of safety and maintenance team, in case of alerts and, finally to reduce overflows. Among the different services implemented on the WEC platform, ARIA have developed a product having the goal to support sewage/water distribution operators, based on a gradual forecast information system ( at 48hrs/24hrs/12hrs horizons) of heavy precipitation. For each fixed deadline different type of operation are implemented: 1) 48hour horizon, organisation of "on call team", 2) 24 hour horizon, update and confirm the "on call team", 3) 12 hour horizon, secure human resources and equipment (emptying storage basins, pipes manipulations …). More specifically CMCC have provided a statistical downscaling method in order to provide a "rough" daily local precipitation at 24 hours, especially when high precipitation values are expected. This statistical technique consists of an adaptation of analogue method based on ECMWF data (analysis and forecast at 24 hours). One of the most advantages of this technique concerns a lower computational burden and budget compared to running a Numerical Weather Prediction (NWP) model, also if, of course it provides only this
Directory of Open Access Journals (Sweden)
MIHAELA BRATU (SIMIONESCU
2012-12-01
Full Text Available In this study some alternative forecasts for the unemployment rate of USA made by four institutions (International Monetary Fund (IMF, Organization for Economic Co-operation and Development (OECD, Congressional Budget Office (CBO and Blue Chips (BC are evaluated regarding the accuracy and the biasness. The most accurate predictions on the forecasting horizon 201-2011 were provided by IMF, followed by OECD, CBO and BC.. These results were gotten using U1 Theil’s statistic and a new method that has not been used before in literature in this context. The multi-criteria ranking was applied to make a hierarchy of the institutions regarding the accuracy and five important accuracy measures were taken into account at the same time: mean errors, mean squared error, root mean squared error, U1 and U2 statistics of Theil. The IMF, OECD and CBO predictions are unbiased. The combined forecasts of institutions’ predictions are a suitable strategy to improve the forecasts accuracy of IMF and OECD forecasts when all combination schemes are used, but INV one is the best. The filtered and smoothed original predictions based on Hodrick-Prescott filter, respectively Holt-Winters technique are a good strategy of improving only the BC expectations. The proposed strategies to improve the accuracy do not solve the problem of biasness. The assessment and improvement of forecasts accuracy have an important contribution in growing the quality of decisional process.
Experimental methods of shock wave research
Seiler, Friedrich
2016-01-01
This comprehensive and carefully edited volume presents a variety of experimental methods used in Shock Waves research. In 14 self contained chapters this 9th volume of the “Shock Wave Science and Technology Reference Library” presents the experimental methods used in Shock Tubes, Shock Tunnels and Expansion Tubes facilities. Also described is their set-up and operation. The uses of an arc heated wind tunnel and a gun tunnel are also contained in this volume. Whenever possible, in addition to the technical description some typical scientific results obtained using such facilities are described. Additionally, this authoritative book includes techniques for measuring physical properties of blast waves and laser generated shock waves. Information about active shock wave laboratories at different locations around the world that are not described in the chapters herein is given in the Appendix, making this book useful for every researcher involved in shock/blast wave phenomena.
A Novel Flood Forecasting Method Based on Initial State Variable Correction
Directory of Open Access Journals (Sweden)
Kuang Li
2017-12-01
Full Text Available The influence of initial state variables on flood forecasting accuracy by using conceptual hydrological models is analyzed in this paper and a novel flood forecasting method based on correction of initial state variables is proposed. The new method is abbreviated as ISVC (Initial State Variable Correction. The ISVC takes the residual between the measured and forecasted flows during the initial period of the flood event as the objective function, and it uses a particle swarm optimization algorithm to correct the initial state variables, which are then used to drive the flood forecasting model. The historical flood events of 11 watersheds in south China are forecasted and verified, and important issues concerning the ISVC application are then discussed. The study results show that the ISVC is effective and applicable in flood forecasting tasks. It can significantly improve the flood forecasting accuracy in most cases.
High-frequency Rayleigh-wave method
Xia, J.; Miller, R.D.; Xu, Y.; Luo, Y.; Chen, C.; Liu, J.; Ivanov, J.; Zeng, C.
2009-01-01
High-frequency (???2 Hz) Rayleigh-wave data acquired with a multichannel recording system have been utilized to determine shear (S)-wave velocities in near-surface geophysics since the early 1980s. This overview article discusses the main research results of high-frequency surface-wave techniques achieved by research groups at the Kansas Geological Survey and China University of Geosciences in the last 15 years. The multichannel analysis of surface wave (MASW) method is a non-invasive acoustic approach to estimate near-surface S-wave velocity. The differences between MASW results and direct borehole measurements are approximately 15% or less and random. Studies show that simultaneous inversion with higher modes and the fundamental mode can increase model resolution and an investigation depth. The other important seismic property, quality factor (Q), can also be estimated with the MASW method by inverting attenuation coefficients of Rayleigh waves. An inverted model (S-wave velocity or Q) obtained using a damped least-squares method can be assessed by an optimal damping vector in a vicinity of the inverted model determined by an objective function, which is the trace of a weighted sum of model-resolution and model-covariance matrices. Current developments include modeling high-frequency Rayleigh-waves in near-surface media, which builds a foundation for shallow seismic or Rayleigh-wave inversion in the time-offset domain; imaging dispersive energy with high resolution in the frequency-velocity domain and possibly with data in an arbitrary acquisition geometry, which opens a door for 3D surface-wave techniques; and successfully separating surface-wave modes, which provides a valuable tool to perform S-wave velocity profiling with high-horizontal resolution. ?? China University of Geosciences (Wuhan) and Springer-Verlag GmbH 2009.
ENSEMBLE methods to reconcile disparate national long range dispersion forecasts
Mikkelsen, Torben; Galmarini, S.; Bianconi, R.; French, S.
2003-01-01
ENSEMBLE is a web-based decision support system for real-time exchange and evaluation of national long-range dispersion forecasts of nuclear releases with cross-boundary consequences. The system is developed with the purpose to reconcile among disparatenational forecasts for long-range dispersion. ENSEMBLE addresses the problem of achieving a common coherent strategy across European national emergency management when national long-range dispersion forecasts differ from one another during an a...
Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method
Directory of Open Access Journals (Sweden)
Wen-Yeau Chang
2013-09-01
Full Text Available High penetration of wind power in the electricity system provides many challenges to power system operators, mainly due to the unpredictability and variability of wind power generation. Although wind energy may not be dispatched, an accurate forecasting method of wind speed and power generation can help power system operators reduce the risk of an unreliable electricity supply. This paper proposes an enhanced particle swarm optimization (EPSO based hybrid forecasting method for short-term wind power forecasting. The hybrid forecasting method combines the persistence method, the back propagation neural network, and the radial basis function (RBF neural network. The EPSO algorithm is employed to optimize the weight coefficients in the hybrid forecasting method. To demonstrate the effectiveness of the proposed method, the method is tested on the practical information of wind power generation of a wind energy conversion system (WECS installed on the Taichung coast of Taiwan. Comparisons of forecasting performance are made with the individual forecasting methods. Good agreements between the realistic values and forecasting values are obtained; the test results show the proposed forecasting method is accurate and reliable.
Forecasting gaming revenues in Clark County, Nevada: Issues and methods
Energy Technology Data Exchange (ETDEWEB)
Edwards, B.K.; Bando, A.
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. Is is designed to generate forecasts of gaming revenues in Clark County, whose regional economy is dominated by the gaming industry. The model is meant to forecast Clark County gaming revenues and identifies the exogenous variables that affect gaming revenues. It will provide baseline forecasts of Clark County gaming revenues in order to assess changes in gaming-related economic activity resulting from changes in regional economic activity and tourism.
Forecasting gaming revenues in Clark County, Nevada: Issues and methods
Energy Technology Data Exchange (ETDEWEB)
Edwards, B.K.; Bando, A.
1992-07-01
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. Is is designed to generate forecasts of gaming revenues in Clark County, whose regional economy is dominated by the gaming industry. The model is meant to forecast Clark County gaming revenues and identifies the exogenous variables that affect gaming revenues. It will provide baseline forecasts of Clark County gaming revenues in order to assess changes in gaming-related economic activity resulting from changes in regional economic activity and tourism.
Implementation and test of a coastal forecasting system for wind waves in the Mediterranean Sea
Inghilesi, R.; Catini, F.; Orasi, A.; Corsini, S.
2010-09-01
A coastal forecasting system has been implemented in order to provide a coverage of the whole Mediterranean Sea and of several enclosed coastal areas as well. The problem is to achieve a good definition of the small scale coastal processes which affect the propagation of waves toward the shores while retaining the possibility of selecting any of the possible coastal areas in the whole Mediterranean Sea. The system is built on a very high resolution parallel implementation of the WAM and SWAN models, one-way chain-nested in key areas. The system will shortly be part of the ISPRA SIMM forecasting system which has been operative since 2001. The SIMM sistem makes available the high resolution wind fields (0.1/0.1 deg) used in the coastal system. The coastal system is being tested on several Italian coastal areas (Ligurian Sea, Lower Tyrrenian Sea, Sicily Channel, Lower Adriatic Sea) in order to optimise the numerics of the coastal processes and to verify the results in shallow waters and complex bathymetries. The results of the comparison between hindcast and buoy data in very shallow (14m depth) and deep sea (150m depth) will be shown for several episodes in the upper Tyrrenian Sea.
Papacharalampous, Georgia; Tyralis, Hristos; Koutsoyiannis, Demetris
2017-04-01
Machine learning (ML) is considered to be a promising approach to hydrological processes forecasting. We conduct a comparison between several stochastic and ML point estimation methods by performing large-scale computational experiments based on simulations. The purpose is to provide generalized results, while the respective comparisons in the literature are usually based on case studies. The stochastic methods used include simple methods, models from the frequently used families of Autoregressive Moving Average (ARMA), Autoregressive Fractionally Integrated Moving Average (ARFIMA) and Exponential Smoothing models. The ML methods used are Random Forests (RF), Support Vector Machines (SVM) and Neural Networks (NN). The comparison refers to the multi-step ahead forecasting properties of the methods. A total of 20 methods are used, among which 9 are the ML methods. 12 simulation experiments are performed, while each of them uses 2 000 simulated time series of 310 observations. The time series are simulated using stochastic processes from the families of ARMA and ARFIMA models. Each time series is split into a fitting (first 300 observations) and a testing set (last 10 observations). The comparative assessment of the methods is based on 18 metrics, that quantify the methods' performance according to several criteria related to the accurate forecasting of the testing set, the capturing of its variation and the correlation between the testing and forecasted values. The most important outcome of this study is that there is not a uniformly better or worse method. However, there are methods that are regularly better or worse than others with respect to specific metrics. It appears that, although a general ranking of the methods is not possible, their classification based on their similar or contrasting performance in the various metrics is possible to some extent. Another important conclusion is that more sophisticated methods do not necessarily provide better forecasts
DEFF Research Database (Denmark)
Ferri, Francesco; Sichani, Mahdi Teimouri; Frigaard, Peter
2012-01-01
Short-term wave forecasting plays a crucial role for the control of a wave energy converter (WEC), in order to increase the energy harvest from the waves, as well as to increase its life time. In the paper it is shown how the surface elevation of the waves and the force acting on the WEC can be p...
Real time wave forecasting using wind time history and numerical model
Jain, Pooja; Deo, M. C.; Latha, G.; Rajendran, V.
Operational activities in the ocean like planning for structural repairs or fishing expeditions require real time prediction of waves over typical time duration of say a few hours. Such predictions can be made by using a numerical model or a time series model employing continuously recorded waves. This paper presents another option to do so and it is based on a different time series approach in which the input is in the form of preceding wind speed and wind direction observations. This would be useful for those stations where the costly wave buoys are not deployed and instead only meteorological buoys measuring wind are moored. The technique employs alternative artificial intelligence approaches of an artificial neural network (ANN), genetic programming (GP) and model tree (MT) to carry out the time series modeling of wind to obtain waves. Wind observations at four offshore sites along the east coast of India were used. For calibration purpose the wave data was generated using a numerical model. The predicted waves obtained using the proposed time series models when compared with the numerically generated waves showed good resemblance in terms of the selected error criteria. Large differences across the chosen techniques of ANN, GP, MT were not noticed. Wave hindcasting at the same time step and the predictions over shorter lead times were better than the predictions over longer lead times. The proposed method is a cost effective and convenient option when a site-specific information is desired.
General method for designing wave shape transformers.
Ma, Hua; Qu, Shaobo; Xu, Zhuo; Wang, Jiafu
2008-12-22
An effective method for designing wave shape transformers (WSTs) is investigated by adopting the coordinate transformation theory. Following this method, the devices employed to transform electromagnetic (EM) wave fronts from one style with arbitrary shape and size to another style, can be designed. To verify this method, three examples in 2D spaces are also presented. Compared with the methods proposed in other literatures, this method offers the general procedure in designing WSTs, and thus is of great importance for the potential and practical applications possessed by such kinds of devices.
Comparison between ARIMA and DES Methods of Forecasting Population for Housing Demand in Johor
Directory of Open Access Journals (Sweden)
Alias Ahmad Rizal
2016-01-01
Full Text Available Forecasting accuracy is a primary criterion in selecting appropriate method of prediction. Even though there are various methods of forecasting however not all of these methods are able to predict with good accuracy. This paper presents an evaluation of two methods of population forecasting for housing demand. These methods are Autoregressive Integrated Moving Average (ARIMA and Double Exponential Smoothing (DES. Both of the methods are principally adopting univariate time series analysis which uses past and present data for forecasting. Secondary data obtained from Department of Statistics, Malaysia was used to forecast population for housing demand in Johor. Forecasting processes had generated 14 models to each of the methods and these models where evaluated using Mean Absolute Percentage Error (MAPE. It was found that 14 of Double Exponential Smoothing models and also 14 of ARIMA models had resulted to 1.674% and 5.524% of average MAPE values respectively. Hence, the Double Exponential Smoothing method outperformed the ARIMA method by reducing 4.00 % in forecasting model population for Johor state. These findings help researchers and government agency in selecting appropriate forecasting model for housing demand.
Breivik, Øyvind; Alves, Jose Henrique; Greenslade, Diana; Horsburgh, Kevin; Swail, Val
2017-04-01
Following the 14th International Workshop on Wave Hindcasting and Forecasting and 5th Coastal Hazards Symposium in November 2014 in Key West, Florida, a topical collection has appeared in recent issues of Ocean Dynamics. Here, we give a brief overview of the 16 papers published in this topical collection as well as an overview of the widening scope of the conference in recent years. A general trend in the field has been towards closer integration between the wave and ocean modelling communities. This is also seen in this topical collection, with several papers exploring the interaction between surface waves and mixed layer dynamics and sea ice.
Comparison of Adaline and Multiple Linear Regression Methods for Rainfall Forecasting
Sutawinaya, IP; Astawa, INGA; Hariyanti, NKD
2018-01-01
Heavy rainfall can cause disaster, therefore need a forecast to predict rainfall intensity. Main factor that cause flooding is there is a high rainfall intensity and it makes the river become overcapacity. This will cause flooding around the area. Rainfall factor is a dynamic factor, so rainfall is very interesting to be studied. In order to support the rainfall forecasting, there are methods that can be used from Artificial Intelligence (AI) to statistic. In this research, we used Adaline for AI method and Regression for statistic method. The more accurate forecast result shows the method that used is good for forecasting the rainfall. Through those methods, we expected which is the best method for rainfall forecasting here.
A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method.
Yang, Jun-He; Cheng, Ching-Hsue; Chan, Chia-Pan
2017-01-01
Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir's water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir's water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.
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.
Suryanarayana, Gowri; Lago Garcia, J.; Geysen, Davy; Aleksiejuk, Piotr; Johansson, Christian
2018-01-01
Recent research has seen several forecasting methods being applied for heat load forecasting of district heating networks. This paper presents two methods that gain significant improvements compared to the previous works. First, an automated way of handling non-linear dependencies in linear
Forecasting with quantitative methods the impact of special events in time series
Nikolopoulos, Konstantinos
2010-01-01
Abstract Quantitative methods are very successful for producing baseline forecasts of time series; however these models fail to forecast neither the timing nor the impact of special events such as promotions or strikes. In most of the cases the timing of such events is not known so they are usually referred as shocks (economics) or special events (forecasting). Sometimes the timing of such events is known a priori (i.e. a future promotion); but even then the impact of the forthcom...
Reservoir water level forecasting using group method of data handling
Zaji, Amir Hossein; Bonakdari, Hossein; Gharabaghi, Bahram
2018-06-01
Accurately forecasted reservoir water level is among the most vital data for efficient reservoir structure design and management. In this study, the group method of data handling is combined with the minimum description length method to develop a very practical and functional model for predicting reservoir water levels. The models' performance is evaluated using two groups of input combinations based on recent days and recent weeks. Four different input combinations are considered in total. The data collected from Chahnimeh#1 Reservoir in eastern Iran are used for model training and validation. To assess the models' applicability in practical situations, the models are made to predict a non-observed dataset for the nearby Chahnimeh#4 Reservoir. According to the results, input combinations (L, L -1) and (L, L -1, L -12) for recent days with root-mean-squared error (RMSE) of 0.3478 and 0.3767, respectively, outperform input combinations (L, L -7) and (L, L -7, L -14) for recent weeks with RMSE of 0.3866 and 0.4378, respectively, with the dataset from https://www.typingclub.com/st. Accordingly, (L, L -1) is selected as the best input combination for making 7-day ahead predictions of reservoir water levels.
Surface wave velocity tracking by bisection method
International Nuclear Information System (INIS)
Maeda, T.
2005-01-01
Calculation of surface wave velocity is a classic problem dating back to the well-known Haskell's transfer matrix method, which contributes to solutions of elastic wave propagation, global subsurface structure evaluation by simulating observed earthquake group velocities, and on-site evaluation of subsurface structure by simulating phase velocity dispersion curves and/or H/V spectra obtained by micro-tremor observation. Recently inversion analysis on micro-tremor observation requires efficient method of generating many model candidates and also stable, accurate, and fast computation of dispersion curves and Raleigh wave trajectory. The original Haskell's transfer matrix method has been improved in terms of its divergence tendency mainly by the generalized transmission and reflection matrix method with formulation available for surface wave velocity; however, root finding algorithm has not been fully discussed except for the one by setting threshold to the absolute value of complex characteristic functions. Since surface wave number (reciprocal to the surface wave velocity multiplied by frequency) is a root of complex valued characteristic function, it is intractable to use general root finding algorithm. We will examine characteristic function in phase plane to construct two dimensional bisection algorithm with consideration on a layer to be evaluated and algorithm for tracking roots down along frequency axis. (author)
Wang, Ziyin; Liu, Mandan; Cheng, Yicheng; Wang, Rubin
2017-06-01
In this paper, a dynamical recurrent artificial neural network (ANN) is proposed and studied. Inspired from a recent research in neuroscience, we introduced nonsynaptic coupling to form a dynamical component of the network. We mathematically proved that, with adequate neurons provided, this dynamical ANN model is capable of approximating any continuous dynamic system with an arbitrarily small error in a limited time interval. Its extreme concise Jacobian matrix makes the local stability easy to control. We designed this ANN for fitting and forecasting dynamic data and obtained satisfied results in simulation. The fitting performance is also compared with those of both the classic dynamic ANN and the state-of-the-art models. Sufficient trials and the statistical results indicated that our model is superior to those have been compared. Moreover, we proposed a robust approximation problem, which asking the ANN to approximate a cluster of input-output data pairs in large ranges and to forecast the output of the system under previously unseen input. Our model and learning scheme proposed in this paper have successfully solved this problem, and through this, the approximation becomes much more robust and adaptive to noise, perturbation, and low-order harmonic wave. This approach is actually an efficient method for compressing massive external data of a dynamic system into the weight of the ANN.
Probabilistic Wind Power Ramp Forecasting Based on a Scenario Generation Method
Energy Technology Data Exchange (ETDEWEB)
Wang, Qin [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Florita, Anthony R [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Krishnan, Venkat K [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Hodge, Brian S [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Cui, Mingjian [University of Texas at Dallas; Feng, Cong [University of Texas at Dallas; Wang, Zhenke [University of Texas at Dallas; Zhang, Jie [University of Texas at Dallas
2018-02-01
Wind power ramps (WPRs) are particularly important in the management and dispatch of wind power and currently drawing the attention of balancing authorities. With the aim to reduce the impact of WPRs for power system operations, this paper develops a probabilistic ramp forecasting method based on a large number of simulated scenarios. An ensemble machine learning technique is first adopted to forecast the basic wind power forecasting scenario and calculate the historical forecasting errors. A continuous Gaussian mixture model (GMM) is used to fit the probability distribution function (PDF) of forecasting errors. The cumulative distribution function (CDF) is analytically deduced. The inverse transform method based on Monte Carlo sampling and the CDF is used to generate a massive number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The probabilistic forecasting results of ramp duration and start-time are generated based on all scenarios. Numerical simulations on publicly available wind power data show that within a predefined tolerance level, the developed probabilistic wind power ramp forecasting method is able to predict WPRs with a high level of sharpness and accuracy.
Probabilistic Wind Power Ramp Forecasting Based on a Scenario Generation Method: Preprint
Energy Technology Data Exchange (ETDEWEB)
Wang, Qin [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Florita, Anthony R [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Krishnan, Venkat K [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Hodge, Brian S [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Cui, Mingjian [Univ. of Texas-Dallas, Richardson, TX (United States); Feng, Cong [Univ. of Texas-Dallas, Richardson, TX (United States); Wang, Zhenke [Univ. of Texas-Dallas, Richardson, TX (United States); Zhang, Jie [Univ. of Texas-Dallas, Richardson, TX (United States)
2017-08-31
Wind power ramps (WPRs) are particularly important in the management and dispatch of wind power, and they are currently drawing the attention of balancing authorities. With the aim to reduce the impact of WPRs for power system operations, this paper develops a probabilistic ramp forecasting method based on a large number of simulated scenarios. An ensemble machine learning technique is first adopted to forecast the basic wind power forecasting scenario and calculate the historical forecasting errors. A continuous Gaussian mixture model (GMM) is used to fit the probability distribution function (PDF) of forecasting errors. The cumulative distribution function (CDF) is analytically deduced. The inverse transform method based on Monte Carlo sampling and the CDF is used to generate a massive number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The probabilistic forecasting results of ramp duration and start time are generated based on all scenarios. Numerical simulations on publicly available wind power data show that within a predefined tolerance level, the developed probabilistic wind power ramp forecasting method is able to predict WPRs with a high level of sharpness and accuracy.
ENSEMBLE methods to reconcile disparate national long range dispersion forecasts
DEFF Research Database (Denmark)
Mikkelsen, Torben; Galmarini, S.; Bianconi, R.
2003-01-01
ENSEMBLE is a web-based decision support system for real-time exchange and evaluation of national long-range dispersion forecasts of nuclear releases with cross-boundary consequences. The system is developed with the purpose to reconcile among disparatenational forecasts for long-range dispersion...... emergency and meteorological forecasting centres, which may choose to integrate them directly intooperational emergency information systems, or possibly use them as a basis for future system development.......ENSEMBLE is a web-based decision support system for real-time exchange and evaluation of national long-range dispersion forecasts of nuclear releases with cross-boundary consequences. The system is developed with the purpose to reconcile among disparatenational forecasts for long-range dispersion....... ENSEMBLE addresses the problem of achieving a common coherent strategy across European national emergency management when national long-range dispersion forecasts differ from one another during an accidentalatmospheric release of radioactive material. A series of new decision-making “ENSEMBLE” procedures...
Wave propagation retrieval method for chiral metamaterials
DEFF Research Database (Denmark)
Andryieuski, Andrei; Malureanu, Radu; Lavrinenko, Andrei
2010-01-01
In this paper we present the wave propagation method for the retrieving of effective properties of media with circularly polarized eigenwaves, in particularly for chiral metamaterials. The method is applied for thick slabs and provides bulk effective parameters. Its strong sides are the absence...
Timmermann, Allan G
2005-01-01
Forecast combinations have frequently been found in empirical studies to produce better forecasts on average than methods based on the ex-ante best individual forecasting model. Moreover, simple combinations that ignore correlations between forecast errors often dominate more refined combination schemes aimed at estimating the theoretically optimal combination weights. In this paper we analyse theoretically the factors that determine the advantages from combining forecasts (for example, the d...
Evaluation of bias-correction methods for ensemble streamflow volume forecasts
Directory of Open Access Journals (Sweden)
T. Hashino
2007-01-01
Full Text Available Ensemble prediction systems are used operationally to make probabilistic streamflow forecasts for seasonal time scales. However, hydrological models used for ensemble streamflow prediction often have simulation biases that degrade forecast quality and limit the operational usefulness of the forecasts. This study evaluates three bias-correction methods for ensemble streamflow volume forecasts. All three adjust the ensemble traces using a transformation derived with simulated and observed flows from a historical simulation. The quality of probabilistic forecasts issued when using the three bias-correction methods is evaluated using a distributions-oriented verification approach. Comparisons are made of retrospective forecasts of monthly flow volumes for a north-central United States basin (Des Moines River, Iowa, issued sequentially for each month over a 48-year record. The results show that all three bias-correction methods significantly improve forecast quality by eliminating unconditional biases and enhancing the potential skill. Still, subtle differences in the attributes of the bias-corrected forecasts have important implications for their use in operational decision-making. Diagnostic verification distinguishes these attributes in a context meaningful for decision-making, providing criteria to choose among bias-correction methods with comparable skill.
ENSEMBLE methods to reconcile disparate national long range dispersion forecasting
Energy Technology Data Exchange (ETDEWEB)
Mikkelsen, T; Galmarini, S; Bianconi, R; French, S [eds.
2003-11-01
ENSEMBLE is a web-based decision support system for real-time exchange and evaluation of national long-range dispersion forecasts of nuclear releases with cross-boundary consequences. The system is developed with the purpose to reconcile among disparate national forecasts for long-range dispersion. ENSEMBLE addresses the problem of achieving a common coherent strategy across European national emergency management when national long-range dispersion forecasts differ from one another during an accidental atmospheric release of radioactive material. A series of new decision-making 'ENSEMBLE' procedures and Web-based software evaluation and exchange tools have been created for real-time reconciliation and harmonisation of real-time dispersion forecasts from meteorological and emergency centres across Europe during an accident. The new ENSEMBLE software tools is available to participating national emergency and meteorological forecasting centres, which may choose to integrate them directly into operational emergency information systems, or possibly use them as a basis for future system development. (au)
ENSEMBLE methods to reconcile disparate national long range dispersion forecasting
Energy Technology Data Exchange (ETDEWEB)
Mikkelsen, T.; Galmarini, S.; Bianconi, R.; French, S. (eds.)
2003-11-01
ENSEMBLE is a web-based decision support system for real-time exchange and evaluation of national long-range dispersion forecasts of nuclear releases with cross-boundary consequences. The system is developed with the purpose to reconcile among disparate national forecasts for long-range dispersion. ENSEMBLE addresses the problem of achieving a common coherent strategy across European national emergency management when national long-range dispersion forecasts differ from one another during an accidental atmospheric release of radioactive material. A series of new decision-making 'ENSEMBLE' procedures and Web-based software evaluation and exchange tools have been created for real-time reconciliation and harmonisation of real-time dispersion forecasts from meteorological and emergency centres across Europe during an accident. The new ENSEMBLE software tools is available to participating national emergency and meteorological forecasting centres, which may choose to integrate them directly into operational emergency information systems, or possibly use them as a basis for future system development. (au)
Skill forecasting from different wind power ensemble prediction methods
International Nuclear Information System (INIS)
Pinson, Pierre; Nielsen, Henrik A; Madsen, Henrik; Kariniotakis, George
2007-01-01
This paper presents an investigation on alternative approaches to the providing of uncertainty estimates associated to point predictions of wind generation. Focus is given to skill forecasts in the form of prediction risk indices, aiming at giving a comprehensive signal on the expected level of forecast uncertainty. Ensemble predictions of wind generation are used as input. A proposal for the definition of prediction risk indices is given. Such skill forecasts are based on the dispersion of ensemble members for a single prediction horizon, or over a set of successive look-ahead times. It is shown on the test case of a Danish offshore wind farm how prediction risk indices may be related to several levels of forecast uncertainty (and energy imbalances). Wind power ensemble predictions are derived from the transformation of ECMWF and NCEP ensembles of meteorological variables to power, as well as by a lagged average approach alternative. The ability of risk indices calculated from the various types of ensembles forecasts to resolve among situations with different levels of uncertainty is discussed
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
Multitask Learning-Based Security Event Forecast Methods for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Hui He
2016-01-01
Full Text Available Wireless sensor networks have strong dynamics and uncertainty, including network topological changes, node disappearance or addition, and facing various threats. First, to strengthen the detection adaptability of wireless sensor networks to various security attacks, a region similarity multitask-based security event forecast method for wireless sensor networks is proposed. This method performs topology partitioning on a large-scale sensor network and calculates the similarity degree among regional subnetworks. The trend of unknown network security events can be predicted through multitask learning of the occurrence and transmission characteristics of known network security events. Second, in case of lacking regional data, the quantitative trend of unknown regional network security events can be calculated. This study introduces a sensor network security event forecast method named Prediction Network Security Incomplete Unmarked Data (PNSIUD method to forecast missing attack data in the target region according to the known partial data in similar regions. Experimental results indicate that for an unknown security event forecast the forecast accuracy and effects of the similarity forecast algorithm are better than those of single-task learning method. At the same time, the forecast accuracy of the PNSIUD method is better than that of the traditional support vector machine method.
A new cascade NN based method to short-term load forecast in deregulated electricity market
International Nuclear Information System (INIS)
Kouhi, Sajjad; Keynia, Farshid
2013-01-01
Highlights: • We are proposed a new hybrid cascaded NN based method and WT to short-term load forecast in deregulated electricity market. • An efficient preprocessor consist of normalization and shuffling of signals is presented. • In order to select the best inputs, a two-stage feature selection is presented. • A new cascaded structure consist of three cascaded NNs is used as forecaster. - Abstract: Short-term load forecasting (STLF) is a major discussion in efficient operation of power systems. The electricity load is a nonlinear signal with time dependent behavior. The area of electricity load forecasting has still essential need for more accurate and stable load forecast algorithm. To improve the accuracy of prediction, a new hybrid forecast strategy based on cascaded neural network is proposed for STLF. This method is consists of wavelet transform, an intelligent two-stage feature selection, and cascaded neural network. The feature selection is used to remove the irrelevant and redundant inputs. The forecast engine is composed of three cascaded neural network (CNN) structure. This cascaded structure can be efficiently extract input/output mapping function of the nonlinear electricity load data. Adjustable parameters of the intelligent feature selection and CNN is fine-tuned by a kind of cross-validation technique. The proposed STLF is tested on PJM and New York electricity markets. It is concluded from the result, the proposed algorithm is a robust forecast method
Comparison of two methods forecasting binding rate of plasma protein.
Hongjiu, Liu; Yanrong, Hu
2014-01-01
By introducing the descriptors calculated from the molecular structure, the binding rates of plasma protein (BRPP) with seventy diverse drugs are modeled by a quantitative structure-activity relationship (QSAR) technique. Two algorithms, heuristic algorithm (HA) and support vector machine (SVM), are used to establish linear and nonlinear models to forecast BRPP. Empirical analysis shows that there are good performances for HA and SVM with cross-validation correlation coefficients Rcv(2) of 0.80 and 0.83. Comparing HA with SVM, it was found that SVM has more stability and more robustness to forecast BRPP.
Long- Range Forecasting Of The Onset Of Southwest Monsoon Winds And Waves Near The Horn Of Africa
2017-12-01
conditions is also indicated ( S : strong, M: moderate, W: weak). ..............34 xi LIST OF TABLES Table 1. Table of correlation experiments conducted...2nd ed.). Essex, England: Pearson prentice hall, 317 pp. Saha, S ., and Coauthors, 2010: NCEP Climate Forecast System Reanalysis (CFSR) Selected...WAVES NEAR THE HORN OF AFRICA 5. FUNDING NUMBERS 6. AUTHOR( S ) Gary M. Vines 7. PERFORMING ORGANIZATION NAME( S ) AND ADDRESS(ES) Naval Postgraduate
Directory of Open Access Journals (Sweden)
Ebru ULUCAN
2018-05-01
Full Text Available As it being seen in every sector, demand forecasting in tourism is been conducted with various qualitative and quantitative methods. In recent years, artificial neural network models, which have been developed as an alternative to these forecasting methods, give the nearest values in forecasting with the smallest failure percentage. This study aims to reveal that accomodation establishments can use the neural network models as an alternative while forecasting their demand. With this aim, neural network models have been tested by using the sold room values between the period of 2013-2016 of a five star hotel in Istanbul and it is found that the results acquired from the testing models are the nearest values comparing the realized figures. In the light of these results, tourism demand of the hotel for 2017 and 2018 has been forecasted.
International Nuclear Information System (INIS)
Monjoly, Stéphanie; André, Maïna; Calif, Rudy; Soubdhan, Ted
2017-01-01
This paper introduces a new approach for the forecasting of solar radiation series at 1 h ahead. We investigated on several techniques of multiscale decomposition of clear sky index K_c data such as Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) and Wavelet Decomposition. From these differents methods, we built 11 decomposition components and 1 residu signal presenting different time scales. We performed classic forecasting models based on linear method (Autoregressive process AR) and a non linear method (Neural Network model). The choice of forecasting method is adaptative on the characteristic of each component. Hence, we proposed a modeling process which is built from a hybrid structure according to the defined flowchart. An analysis of predictive performances for solar forecasting from the different multiscale decompositions and forecast models is presented. From multiscale decomposition, the solar forecast accuracy is significantly improved, particularly using the wavelet decomposition method. Moreover, multistep forecasting with the proposed hybrid method resulted in additional improvement. For example, in terms of RMSE error, the obtained forecasting with the classical NN model is about 25.86%, this error decrease to 16.91% with the EMD-Hybrid Model, 14.06% with the EEMD-Hybid model and to 7.86% with the WD-Hybrid Model. - Highlights: • Hourly forecasting of GHI in tropical climate with many cloud formation processes. • Clear sky Index decomposition using three multiscale decomposition methods. • Combination of multiscale decomposition methods with AR-NN models to predict GHI. • Comparison of the proposed hybrid model with the classical models (AR, NN). • Best results using Wavelet-Hybrid model in comparison with classical models.
A stochastic post-processing method for solar irradiance forecasts derived from NWPs models
Lara-Fanego, V.; Pozo-Vazquez, D.; Ruiz-Arias, J. A.; Santos-Alamillos, F. J.; Tovar-Pescador, J.
2010-09-01
Solar irradiance forecast is an important area of research for the future of the solar-based renewable energy systems. Numerical Weather Prediction models (NWPs) have proved to be a valuable tool for solar irradiance forecasting with lead time up to a few days. Nevertheless, these models show low skill in forecasting the solar irradiance under cloudy conditions. Additionally, climatic (averaged over seasons) aerosol loading are usually considered in these models, leading to considerable errors for the Direct Normal Irradiance (DNI) forecasts during high aerosols load conditions. In this work we propose a post-processing method for the Global Irradiance (GHI) and DNI forecasts derived from NWPs. Particularly, the methods is based on the use of Autoregressive Moving Average with External Explanatory Variables (ARMAX) stochastic models. These models are applied to the residuals of the NWPs forecasts and uses as external variables the measured cloud fraction and aerosol loading of the day previous to the forecast. The method is evaluated for a set one-moth length three-days-ahead forecast of the GHI and DNI, obtained based on the WRF mesoscale atmospheric model, for several locations in Andalusia (Southern Spain). The Cloud fraction is derived from MSG satellite estimates and the aerosol loading from the MODIS platform estimates. Both sources of information are readily available at the time of the forecast. Results showed a considerable improvement of the forecasting skill of the WRF model using the proposed post-processing method. Particularly, relative improvement (in terms of the RMSE) for the DNI during summer is about 20%. A similar value is obtained for the GHI during the winter.
Comparison of Statistical Post-Processing Methods for Probabilistic Wind Speed Forecasting
Han, Keunhee; Choi, JunTae; Kim, Chansoo
2018-02-01
In this study, the statistical post-processing methods that include bias-corrected and probabilistic forecasts of wind speed measured in PyeongChang, which is scheduled to host the 2018 Winter Olympics, are compared and analyzed to provide more accurate weather information. The six post-processing methods used in this study are as follows: mean bias-corrected forecast, mean and variance bias-corrected forecast, decaying averaging forecast, mean absolute bias-corrected forecast, and the alternative implementations of ensemble model output statistics (EMOS) and Bayesian model averaging (BMA) models, which are EMOS and BMA exchangeable models by assuming exchangeable ensemble members and simplified version of EMOS and BMA models. Observations for wind speed were obtained from the 26 stations in PyeongChang and 51 ensemble member forecasts derived from the European Centre for Medium-Range Weather Forecasts (ECMWF Directorate, 2012) that were obtained between 1 May 2013 and 18 March 2016. Prior to applying the post-processing methods, reliability analysis was conducted by using rank histograms to identify the statistical consistency of ensemble forecast and corresponding observations. Based on the results of our study, we found that the prediction skills of probabilistic forecasts of EMOS and BMA models were superior to the biascorrected forecasts in terms of deterministic prediction, whereas in probabilistic prediction, BMA models showed better prediction skill than EMOS. Even though the simplified version of BMA model exhibited best prediction skill among the mentioned six methods, the results showed that the differences of prediction skills between the versions of EMOS and BMA were negligible.
Mixed price and load forecasting of electricity markets by a new iterative prediction method
International Nuclear Information System (INIS)
Amjady, Nima; Daraeepour, Ali
2009-01-01
Load and price forecasting are the two key issues for the participants of current electricity markets. However, load and price of electricity markets have complex characteristics such as nonlinearity, non-stationarity and multiple seasonality, to name a few (usually, more volatility is seen in the behavior of electricity price signal). For these reasons, much research has been devoted to load and price forecast, especially in the recent years. However, previous research works in the area separately predict load and price signals. In this paper, a mixed model for load and price forecasting is presented, which can consider interactions of these two forecast processes. The mixed model is based on an iterative neural network based prediction technique. It is shown that the proposed model can present lower forecast errors for both load and price compared with the previous separate frameworks. Another advantage of the mixed model is that all required forecast features (from load or price) are predicted within the model without assuming known values for these features. So, the proposed model can better be adapted to real conditions of an electricity market. The forecast accuracy of the proposed mixed method is evaluated by means of real data from the New York and Spanish electricity markets. The method is also compared with some of the most recent load and price forecast techniques. (author)
The Use of Some Forecasting Methods and SWOT Analysis in the Selected Processes of Foundry
Directory of Open Access Journals (Sweden)
Szymszal J.
2017-12-01
Full Text Available Forecasting and analysis SWOT are helping tools in the business activity, because under conditions of dynamic changes in both closer and more distant surroundings, reliable, forward-looking information and trends analysis are playing a decisive role. At present, the ability to use available data in forecasting and other analyzes according with changes in business environment are the key managerial skills required, since both forecasting and SWOT analysis are a integral part of the management process, and the appropriate level of forecasting knowledge is increasingly appreciated. Examples of practical use of some forecasting methods in optimization of the procurement, production and distribution processes in foundries are given. The possibilities of using conventional quantitative forecasting methods based on econometric and adaptive models applying the creep trend and harmonic weights are presented. The econometric models were additionally supplemented with the presentation of error estimation methodology, quality assessment and statistical verification of the forecast. The possibility of using qualitative forecasts based on SWOT analysis was also mentioned.
Systems and methods for wave energy conversion
MacDonald, Daniel G.; Cantara, Justin; Nathan, Craig; Lopes, Amy M.; Green, Brandon E.
2017-02-28
Systems for wave energy conversion that have components that can survive the harsh marine environment and that can be attached to fixed structures, such as a pier, and having the ability to naturally adjust for tidal height and methods for their use are presented.
Review of Wind Energy Forecasting Methods for Modeling Ramping Events
Energy Technology Data Exchange (ETDEWEB)
Wharton, S; Lundquist, J K; Marjanovic, N; Williams, J L; Rhodes, M; Chow, T K; Maxwell, R
2011-03-28
Tall onshore wind turbines, with hub heights between 80 m and 100 m, can extract large amounts of energy from the atmosphere since they generally encounter higher wind speeds, but they face challenges given the complexity of boundary layer flows. This complexity of the lowest layers of the atmosphere, where wind turbines reside, has made conventional modeling efforts less than ideal. To meet the nation's goal of increasing wind power into the U.S. electrical grid, the accuracy of wind power forecasts must be improved. In this report, the Lawrence Livermore National Laboratory, in collaboration with the University of Colorado at Boulder, University of California at Berkeley, and Colorado School of Mines, evaluates innovative approaches to forecasting sudden changes in wind speed or 'ramping events' at an onshore, multimegawatt wind farm. The forecast simulations are compared to observations of wind speed and direction from tall meteorological towers and a remote-sensing Sound Detection and Ranging (SODAR) instrument. Ramping events, i.e., sudden increases or decreases in wind speed and hence, power generated by a turbine, are especially problematic for wind farm operators. Sudden changes in wind speed or direction can lead to large power generation differences across a wind farm and are very difficult to predict with current forecasting tools. Here, we quantify the ability of three models, mesoscale WRF, WRF-LES, and PF.WRF, which vary in sophistication and required user expertise, to predict three ramping events at a North American wind farm.
Statistical tests for equal predictive ability across multiple forecasting methods
DEFF Research Database (Denmark)
Borup, Daniel; Thyrsgaard, Martin
We develop a multivariate generalization of the Giacomini-White tests for equal conditional predictive ability. The tests are applicable to a mixture of nested and non-nested models, incorporate estimation uncertainty explicitly, and allow for misspecification of the forecasting model as well as ...
International Nuclear Information System (INIS)
Abedinia, O.; Amjady, N.; Shafie-khah, M.; Catalão, J.P.S.
2015-01-01
Highlights: • Presenting a Combinatorial Neural Network. • Suggesting a new stochastic search method. • Adapting the suggested method as a training mechanism. • Proposing a new forecast strategy. • Testing the proposed strategy on real-world electricity markets. - Abstract: Electricity price forecast is key information for successful operation of electricity market participants. However, the time series of electricity price has nonlinear, non-stationary and volatile behaviour and so its forecast method should have high learning capability to extract the complex input/output mapping function of electricity price. In this paper, a Combinatorial Neural Network (CNN) based forecasting engine is proposed to predict the future values of price data. The CNN-based forecasting engine is equipped with a new training mechanism for optimizing the weights of the CNN. This training mechanism is based on an efficient stochastic search method, which is a modified version of chemical reaction optimization algorithm, giving high learning ability to the CNN. The proposed price forecast strategy is tested on the real-world electricity markets of Pennsylvania–New Jersey–Maryland (PJM) and mainland Spain and its obtained results are extensively compared with the results obtained from several other forecast methods. These comparisons illustrate effectiveness of the proposed strategy.
Dreier, Norman; Fröhle, Peter
2017-12-01
The knowledge of the wave-induced hydrodynamic loads on coastal dikes including their temporal and spatial resolution on the dike in combination with actual water levels is of crucial importance of any risk-based early warning system. As a basis for the assessment of the wave-induced hydrodynamic loads, an operational wave now- and forecast system is set up that consists of i) available field measurements from the federal and local authorities and ii) data from numerical simulation of waves in the German Bight using the SWAN wave model. In this study, results of the hindcast of deep water wave conditions during the winter storm on 5-6 December, 2013 (German name `Xaver') are shown and compared with available measurements. Moreover field measurements of wave run-up from the local authorities at a sea dike on the German North Sea Island of Pellworm are presented and compared against calculated wave run-up using the EurOtop (2016) approach.
Application Of Multi-grid Method On China Seas' Temperature Forecast
Li, W.; Xie, Y.; He, Z.; Liu, K.; Han, G.; Ma, J.; Li, D.
2006-12-01
Correlation scales have been used in traditional scheme of 3-dimensional variational (3D-Var) data assimilation to estimate the background error covariance for the numerical forecast and reanalysis of atmosphere and ocean for decades. However there are still some drawbacks of this scheme. First, the correlation scales are difficult to be determined accurately. Second, the positive definition of the first-guess error covariance matrix cannot be guaranteed unless the correlation scales are sufficiently small. Xie et al. (2005) indicated that a traditional 3D-Var only corrects some certain wavelength errors and its accuracy depends on the accuracy of the first-guess covariance. And in general, short wavelength error can not be well corrected until long one is corrected and then inaccurate first-guess covariance may mistakenly take long wave error as short wave ones and result in erroneous analysis. For the purpose of quickly minimizing the errors of long and short waves successively, a new 3D-Var data assimilation scheme, called multi-grid data assimilation scheme, is proposed in this paper. By assimilating the shipboard SST and temperature profiles data into a numerical model of China Seas, we applied this scheme in two-month data assimilation and forecast experiment which ended in a favorable result. Comparing with the traditional scheme of 3D-Var, the new scheme has higher forecast accuracy and a lower forecast Root-Mean-Square (RMS) error. Furthermore, this scheme was applied to assimilate the SST of shipboard, AVHRR Pathfinder Version 5.0 SST and temperature profiles at the same time, and a ten-month forecast experiment on sea temperature of China Seas was carried out, in which a successful forecast result was obtained. Particularly, the new scheme is demonstrated a great numerical efficiency in these analyses.
Wang, Jiangbo; Liu, Junhui; Li, Tiantian; Yin, Shuo; He, Xinhui
2018-01-01
The monthly electricity sales forecasting is a basic work to ensure the safety of the power system. This paper presented a monthly electricity sales forecasting method which comprehensively considers the coupled multi-factors of temperature, economic growth, electric power replacement and business expansion. The mathematical model is constructed by using regression method. The simulation results show that the proposed method is accurate and effective.
Heuristic method for determining outgoing waves in many-body wave functions
International Nuclear Information System (INIS)
Redish, E.F.; Tandy, P.C.; L'Huillier, M.
1975-12-01
A new and simple method is proposed for determining the kinds of outgoing waves present in a given many-body wave function. Whether any particular wave function contains ''hidden'' rearrangement components can be determined. 1 figure
Short-Term Wave Forecasting with AR models in Real-Time Optimal Control of Wave Energy Converters
Fusco, Francesco; Ringwood, John
2010-01-01
Time domain control of wave energy converters requires knowledge of future incident wave elevation in order to approach conditions for optimal energy extraction. Autoregressive models revealed to be a promising approach to the prediction of future values of the wave elevation only from its past history. Results on real wave observations from different ocean locations show that AR models allow to achieve very good predictions for more than one wave period in the future if ...
Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method
Wen-Yeau Chang
2013-01-01
High penetration of wind power in the electricity system provides many challenges to power system operators, mainly due to the unpredictability and variability of wind power generation. Although wind energy may not be dispatched, an accurate forecasting method of wind speed and power generation can help power system operators reduce the risk of an unreliable electricity supply. This paper proposes an enhanced particle swarm optimization (EPSO) based hybrid forecasting method for short-term wi...
Year Ahead Demand Forecast of City Natural Gas Using Seasonal Time Series Methods
Directory of Open Access Journals (Sweden)
Mustafa Akpinar
2016-09-01
Full Text Available Consumption of natural gas, a major clean energy source, increases as energy demand increases. We studied specifically the Turkish natural gas market. Turkey’s natural gas consumption increased as well in parallel with the world‘s over the last decade. This consumption growth in Turkey has led to the formation of a market structure for the natural gas industry. This significant increase requires additional investments since a rise in consumption capacity is expected. One of the reasons for the consumption increase is the user-based natural gas consumption influence. This effect yields imbalances in demand forecasts and if the error rates are out of bounds, penalties may occur. In this paper, three univariate statistical methods, which have not been previously investigated for mid-term year-ahead monthly natural gas forecasting, are used to forecast natural gas demand in Turkey’s Sakarya province. Residential and low-consumption commercial data is used, which may contain seasonality. The goal of this paper is minimizing more or less gas tractions on mid-term consumption while improving the accuracy of demand forecasting. In forecasting models, seasonality and single variable impacts reinforce forecasts. This paper studies time series decomposition, Holt-Winters exponential smoothing and autoregressive integrated moving average (ARIMA methods. Here, 2011–2014 monthly data were prepared and divided into two series. The first series is 2011–2013 monthly data used for finding seasonal effects and model requirements. The second series is 2014 monthly data used for forecasting. For the ARIMA method, a stationary series was prepared and transformation process prior to forecasting was done. Forecasting results confirmed that as the computation complexity of the model increases, forecasting accuracy increases with lower error rates. Also, forecasting errors and the coefficients of determination values give more consistent results. Consequently
Tanguy, M.; Prudhomme, C.; Harrigan, S.; Smith, K. A.; Parry, S.
2017-12-01
Forecasting hydrological extremes is challenging, especially at lead times over 1 month for catchments with limited hydrological memory and variable climates. One simple way to derive monthly or seasonal hydrological forecasts is to use historical climate data to drive hydrological models using the Ensemble Streamflow Prediction (ESP) method. This gives a range of possible future streamflow given known initial hydrologic conditions alone. The degree of skill of ESP depends highly on the forecast initialisation month and catchment type. Using dynamic rainfall forecasts as driving data instead of historical data could potentially improve streamflow predictions. A lot of effort is being invested within the meteorological community to improve these forecasts. However, while recent progress shows promise (e.g. NAO in winter), the skill of these forecasts at monthly to seasonal timescales is generally still limited, and the extent to which they might lead to improved hydrological forecasts is an area of active research. Additionally, these meteorological forecasts are currently being produced at 1 month or seasonal time-steps in the UK, whereas hydrological models require forcings at daily or sub-daily time-steps. Keeping in mind these limitations of available rainfall forecasts, the objectives of this study are to find out (i) how accurate monthly dynamical rainfall forecasts need to be to outperform ESP, and (ii) how the method used to disaggregate monthly rainfall forecasts into daily rainfall time series affects results. For the first objective, synthetic rainfall time series were created by increasingly degrading observed data (proxy for a `perfect forecast') from 0 % to +/-50 % error. For the second objective, three different methods were used to disaggregate monthly rainfall data into daily time series. These were used to force a simple lumped hydrological model (GR4J) to generate streamflow predictions at a one-month lead time for over 300 catchments
Holzworth, R. H.; McCarthy, M. P.; Pfaff, R. F.; Jacobson, A. R.; Willcockson, W. L.; Rowland, D. E.
2011-06-01
Direct evidence is presented for a causal relationship between lightning and strong electric field transients inside equatorial ionospheric density depletions. In fact, these whistler mode plasma waves may be the dominant electric field signal within such depletions. Optical lightning data from the Communication/Navigation Outage Forecast System (C/NOFS) satellite and global lightning location information from the World Wide Lightning Location Network are presented as independent verification that these electric field transients are caused by lightning. The electric field instrument on C/NOFS routinely measures lightning-related electric field wave packets or sferics, associated with simultaneous measurements of optical flashes at all altitudes encountered by the satellite (401-867 km). Lightning-generated whistler waves have abundant access to the topside ionosphere, even close to the magnetic equator.
International Nuclear Information System (INIS)
Sumer, Kutluk Kagan; Goktas, Ozlem; Hepsag, Aycan
2009-01-01
In this study, we used ARIMA, seasonal ARIMA (SARIMA) and alternatively the regression model with seasonal latent variable in forecasting electricity demand by using data that belongs to 'Kayseri and Vicinity Electricity Joint-Stock Company' over the 1997:1-2005:12 periods. This study tries to examine the advantages of forecasting with ARIMA, SARIMA methods and with the model has seasonal latent variable to each other. The results support that ARIMA and SARIMA models are unsuccessful in forecasting electricity demand. The regression model with seasonal latent variable used in this study gives more successful results than ARIMA and SARIMA models because also this model can consider seasonal fluctuations and structural breaks
Tourism forecasting using modified empirical mode decomposition and group method of data handling
Yahya, N. A.; Samsudin, R.; Shabri, A.
2017-09-01
In this study, a hybrid model using modified Empirical Mode Decomposition (EMD) and Group Method of Data Handling (GMDH) model is proposed for tourism forecasting. This approach reconstructs intrinsic mode functions (IMFs) produced by EMD using trial and error method. The new component and the remaining IMFs is then predicted respectively using GMDH model. Finally, the forecasted results for each component are aggregated to construct an ensemble forecast. The data used in this experiment are monthly time series data of tourist arrivals from China, Thailand and India to Malaysia from year 2000 to 2016. The performance of the model is evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) where conventional GMDH model and EMD-GMDH model are used as benchmark models. Empirical results proved that the proposed model performed better forecasts than the benchmarked models.
Forecasts on service life by fracture mechanics methods
International Nuclear Information System (INIS)
Munz, D.
1985-01-01
The service life of many component parts can frequently be divided into the stages up to the formation of a crack and of crack propagation. This holds good of fatigue crack, stress corrosion crack, and also in many cases of creep. But often the crack propagation stage is the only one of interest for service life forecasts if cracks must be reckoned with already on putting parts into service. Cracks in welding constructions are typical examples. Crack- and -fracture mechanics deal with the laws underlying crack propagation and provide quantitative information on crack propagation behaviour. (orig./DG) [de
Hansen, J V; Nelson, R D
1997-01-01
Ever since the initial planning for the 1997 Utah legislative session, neural-network forecasting techniques have provided valuable insights for analysts forecasting tax revenues. These revenue estimates are critically important since agency budgets, support for education, and improvements to infrastructure all depend on their accuracy. Underforecasting generates windfalls that concern taxpayers, whereas overforecasting produces budget shortfalls that cause inadequately funded commitments. The pattern finding ability of neural networks gives insightful and alternative views of the seasonal and cyclical components commonly found in economic time series data. Two applications of neural networks to revenue forecasting clearly demonstrate how these models complement traditional time series techniques. In the first, preoccupation with a potential downturn in the economy distracts analysis based on traditional time series methods so that it overlooks an emerging new phenomenon in the data. In this case, neural networks identify the new pattern that then allows modification of the time series models and finally gives more accurate forecasts. In the second application, data structure found by traditional statistical tools allows analysts to provide neural networks with important information that the networks then use to create more accurate models. In summary, for the Utah revenue outlook, the insights that result from a portfolio of forecasts that includes neural networks exceeds the understanding generated from strictly statistical forecasting techniques. In this case, the synergy clearly results in the whole of the portfolio of forecasts being more accurate than the sum of the individual parts.
Vasiljeva, Polina
2016-01-01
In this thesis we revisit the challenging problem of forecasting currency exchange rate. We combine machine learning methods such as agglomerative hierarchical clustering and random forest to construct a two-step approach for predicting movements in currency exchange prices of the Swedish krona and the US dollar. We use a data set with over 200 predictors comprised of different financial and macro-economic time series and their transformations. We perform forecasting for one week ahead with d...
Study on evaluation methods for Rayleigh wave dispersion characteristic
Shi, L.; Tao, X.; Kayen, R.; Shi, H.; Yan, S.
2005-01-01
The evaluation of Rayleigh wave dispersion characteristic is the key step for detecting S-wave velocity structure. By comparing the dispersion curves directly with the spectra analysis of surface waves (SASW) method, rather than comparing the S-wave velocity structure, the validity and precision of microtremor-array method (MAM) can be evaluated more objectively. The results from the China - US joint surface wave investigation in 26 sites in Tangshan, China, show that the MAM has the same precision with SASW method in 83% of the 26 sites. The MAM is valid for Rayleigh wave dispersion characteristic testing and has great application potentiality for site S-wave velocity structure detection.
Elastic wave scattering methods: assessments and suggestions
International Nuclear Information System (INIS)
Gubernatis, J.E.
1985-01-01
The author was asked by the meeting organizers to review and assess the developments over the past ten or so years in elastic wave scattering methods and to suggest areas of future research opportunities. He highlights the developments, focusing on what he feels were distinct steps forward in our theoretical understanding of how elastic waves interact with flaws. For references and illustrative figures, he decided to use as his principal source the proceedings of the various annual Reviews of Progress in Quantitative Nondestructive Evaluation (NDE). These meetings have been the main forum not only for presenting results of theoretical research but also for demonstrating the relevance of the theoretical research for the design and interpretation of experiment. In his opinion a quantitative NDE is possible only if this relevance exists, and his major objective is to discuss and illustrate the degree to which relevance has developed
Real-Time Forecasting System of Winds, Waves and Surge in Tropical Cyclones
National Research Council Canada - National Science Library
Graber, Hans C; Donelan, Mark A; Brown, Michael G; Slinn, Donald N; Hagen, Scott C; Thompson, Donald R; Jensen, Robert E; Black, Peter G; Powell, Mark D; Guiney, John L
2004-01-01
.... The results of this forecasting system would provide real-time information to the National Hurricane Center during the tropical cyclone season in the Atlantic for establishing improved advisories...
Real-Time Forecasting System of Winds, Waves and Surge in Tropical Cyclones
National Research Council Canada - National Science Library
Graber, Hans C; Donelan, Mark A; Brown, Michael G; Slinn, Donald N; Hagen, Scott C; Thompson, Donald R; Jensen, Robert E; Black, Peter G; Powell, Mark D; Guiney, John L; Cardone, Vincent J; Cox, Andrew T; Augustus, Ellsworth H; Colonnese, Christopher P
2003-01-01
.... The results of this forecasting system would provide real-time information to the National Hurricane Center during the tropical cyclone season in the Atlantic for establishing improved advisories...
Real-Time Forecasting System of Winds, Waves and Surge in Tropical Cyclones
National Research Council Canada - National Science Library
Graber, Hans C; Donelan, Mark A; Brown, Michael G; Slinn, Donald N; Hagen, Scott C; Thompson, Donald R; Jensen, Robert E; Black, Peter G; Powell, Mark D; Guiney, John L
2005-01-01
.... The results of this forecasting system would provide real-time information to the National Hurricane Center during the tropical cyclone season in the Atlantic for establishing improved advisories...
Real-Time Forecasting System of Winds, Waves and Surge in Tropical Cyclones
National Research Council Canada - National Science Library
Graber, Hans C; Donelan, Mark A; Brown, Michael G; Slinn, Donald N; Hagen, Scott C; Thompson, Donald R; Jensen, Robert E; Black, Peter G; Powell, Mark D; Guiney, John L; Cardone, Vincent J; Cox, Andrew T
2006-01-01
... of tropical cyclones The results of this forecasting system would provide real-time information to the National Hurricane Center during the tropical cyclone season in the Atlantic for establishing improved...
A travel time forecasting model based on change-point detection method
LI, Shupeng; GUANG, Xiaoping; QIAN, Yongsheng; ZENG, Junwei
2017-06-01
Travel time parameters obtained from road traffic sensors data play an important role in traffic management practice. A travel time forecasting model is proposed for urban road traffic sensors data based on the method of change-point detection in this paper. The first-order differential operation is used for preprocessing over the actual loop data; a change-point detection algorithm is designed to classify the sequence of large number of travel time data items into several patterns; then a travel time forecasting model is established based on autoregressive integrated moving average (ARIMA) model. By computer simulation, different control parameters are chosen for adaptive change point search for travel time series, which is divided into several sections of similar state.Then linear weight function is used to fit travel time sequence and to forecast travel time. The results show that the model has high accuracy in travel time forecasting.
International Nuclear Information System (INIS)
Sambou, Soussou
2004-01-01
In flood forecasting modelling, large basins are often considered as hydrological systems with multiple inputs and one output. Inputs are hydrological variables such rainfall, runoff and physical characteristics of basin; output is runoff. Relating inputs to output can be achieved using deterministic, conceptual, or stochastic models. Rainfall runoff models generally lack of accuracy. Physical hydrological processes based models, either deterministic or conceptual are highly data requirement demanding and by the way very complex. Stochastic multiple input-output models, using only historical chronicles of hydrological variables particularly runoff are by the way very popular among the hydrologists for large river basin flood forecasting. Application is made on the Senegal River upstream of Bakel, where the River is formed by the main branch, Bafing, and two tributaries, Bakoye and Faleme; Bafing being regulated by Manantaly Dam. A three inputs and one output model has been used for flood forecasting on Bakel. Influence of the lead forecasting, and of the three inputs taken separately, then associated two by two, and altogether has been verified using a dimensionless variance as criterion of quality. Inadequacies occur generally between model output and observations; to put model in better compliance with current observations, we have compared four parameter updating procedure, recursive least squares, Kalman filtering, stochastic gradient method, iterative method, and an AR errors forecasting model. A combination of these model updating have been used in real time flood forecasting.(Author)
An approximate method of short-term tsunami forecast and the hindcasting of some recent events
Directory of Open Access Journals (Sweden)
Yu. P. Korolev
2011-11-01
Full Text Available The paper presents a method for a short-term tsunami forecast based on sea level data from remote sites. This method is based on Green's function for the wave equation possessing the fundamental property of symmetry. This property is well known in acoustics and seismology as the reciprocity principle. Some applications of this principle on tsunami research are considered in the current study. Simple relationships and estimated transfer functions enabled us to simulate tsunami waveforms for any selected oceanic point based only on the source location and sea level data from a remote reference site. The important advantage of this method is that it is irrespective of the actual source mechanism (seismic, submarine landslide or other phenomena. The method was successfully applied to hindcast several recent tsunamis observed in the Northwest Pacific. The locations of the earthquake epicenters and the tsunami records from one of the NOAA DART sites were used as inputs for the modelling, while tsunami observations at other DART sites were used to verify the model. Tsunami waveforms for the 2006, 2007 and 2009 earthquake events near Simushir Island were simulated and found to be in good agreement with the observations. The correlation coefficients between the predicted and observed tsunami waveforms were from 0.50 to 0.85. Thus, the proposed method can be effectively used to simulate tsunami waveforms for the entire ocean and also for both regional and local tsunami warning services, assuming that they have access to the real-time sea level data from DART stations.
Application of SVM methods for mid-term load forecasting
Directory of Open Access Journals (Sweden)
Božić Miloš
2011-01-01
Full Text Available This paper presents an approach for the medium-term load forecasting using Support Vector Machines (SVMs. The proposed SVM model was employed to predict the maximum daily load demand for the period of a month. Analyses of available data were performed and the most important features for the construction of SVM model are selected. It was shown that the size and the structure of the training set may significantly affect the accuracy of predictions. The presented model was tested by applying it on real-life load data obtained from distribution company 'ED Jugoistok' for the territory of city Niš and its surroundings. Experimental results show that the proposed approach gives acceptable results for the entire period of prediction, which are in range with other solutions in this area.
Tolosana-Delgado, R.; Soret, A.; Jorba, O.; Baldasano, J. M.; Sánchez-Arcilla, A.
2012-04-01
Meteorological models, like WRF, usually describe the earth surface characteristics by tables that are function of land-use. The roughness length (z0) is an example of such approach. However, over sea z0 is modeled by the Charnock (1955) relation, linking the surface friction velocity u*2 with the roughness length z0 of turbulent air flow, z0 = α-u2* g The Charnock coefficient α may be considered a measure of roughness. For the sea surface, WRF considers a constant roughness α = 0.0185. However, there is evidence that sea surface roughness should depend on wave energy (Donelan, 1982). Spectral wave models like WAM, model the evolution and propagation of wave energy as a function of wind, and include a richer sea surface roughness description. Coupling WRF and WAM is thus a common way to improve the sea surface roughness description of WRF. WAM is a third generation wave model, solving the equation of advection of wave energy subject to input/output terms of: wind growth, energy dissipation and resonant non-linear wave-wave interactions. Third generation models work on the spectral domain. WAM considers the Charnock coefficient α a complex yet known function of the total wind input term, which depends on the wind velocity and on the Charnock coefficient again. This is solved iteratively (Janssen et al., 1990). Coupling of meteorological and wave models through a common Charnock coefficient is operationally done in medium-range met forecasting systems (e.g., at ECMWF) though the impact of coupling for smaller domains is not yet clearly assessed (Warner et al, 2010). It is unclear to which extent the additional effort of coupling improves the local wind and wave fields, in comparison to the effects of other factors, like e.g. a better bathymetry and relief resolution, or a better circulation information which might have its influence on local-scale meteorological processes (local wind jets, local convection, daily marine wind regimes, etc.). This work, within the
International Nuclear Information System (INIS)
Proskuryakov, K.N.; Zaporozhets, M.V.; Fedorov, A.I.
2015-01-01
Forecasting are carried out for external loads in relation to the main circulation circuit - dynamic loads caused by the rotation of the MCP, dynamic loads caused by the earthquake, dynamic loads caused by damage to the MCP in the earthquake. A comparison of the response spectrum of one of the variants of the base of the NPP, with the frequency vibration of the primary circuit equipment for NPP with WWER-1000 and self-frequency of elastic waves in the fluid. Analysis of the comparison results shows that the frequency of vibration of the main equipment of the reactor plant and elastic waves are in the frequency band in the spectrum response corresponding to the maximum amplitude of the seismic action [ru
Seibert, Mathias; Merz, Bruno; Apel, Heiko
2017-03-01
The Limpopo Basin in southern Africa is prone to droughts which affect the livelihood of millions of people in South Africa, Botswana, Zimbabwe and Mozambique. Seasonal drought early warning is thus vital for the whole region. In this study, the predictability of hydrological droughts during the main runoff period from December to May is assessed using statistical approaches. Three methods (multiple linear models, artificial neural networks, random forest regression trees) are compared in terms of their ability to forecast streamflow with up to 12 months of lead time. The following four main findings result from the study. 1. There are stations in the basin at which standardised streamflow is predictable with lead times up to 12 months. The results show high inter-station differences of forecast skill but reach a coefficient of determination as high as 0.73 (cross validated). 2. A large range of potential predictors is considered in this study, comprising well-established climate indices, customised teleconnection indices derived from sea surface temperatures and antecedent streamflow as a proxy of catchment conditions. El Niño and customised indices, representing sea surface temperature in the Atlantic and Indian oceans, prove to be important teleconnection predictors for the region. Antecedent streamflow is a strong predictor in small catchments (with median 42 % explained variance), whereas teleconnections exert a stronger influence in large catchments. 3. Multiple linear models show the best forecast skill in this study and the greatest robustness compared to artificial neural networks and random forest regression trees, despite their capabilities to represent nonlinear relationships. 4. Employed in early warning, the models can be used to forecast a specific drought level. Even if the coefficient of determination is low, the forecast models have a skill better than a climatological forecast, which is shown by analysis of receiver operating characteristics
A new spinning reserve requirement forecast method for deregulated electricity markets
International Nuclear Information System (INIS)
Amjady, Nima; Keynia, Farshid
2010-01-01
Ancillary services are necessary for maintaining the security and reliability of power systems and constitute an important part of trade in competitive electricity markets. Spinning Reserve (SR) is one of the most important ancillary services for saving power system stability and integrity in response to contingencies and disturbances that continuously occur in the power systems. Hence, an accurate day-ahead forecast of SR requirement helps the Independent System Operator (ISO) to conduct a reliable and economic operation of the power system. However, SR signal has complex, non-stationary and volatile behavior along the time domain and depends greatly on system load. In this paper, a new hybrid forecast engine is proposed for SR requirement prediction. The proposed forecast engine has an iterative training mechanism composed of Levenberg-Marquadt (LM) learning algorithm and Real Coded Genetic Algorithm (RCGA), implemented on the Multi-Layer Perceptron (MLP) neural network. The proposed forecast methodology is examined by means of real data of Pennsylvania-New Jersey-Maryland (PJM) electricity market and the California ISO (CAISO) controlled grid. The obtained forecast results are presented and compared with those of the other SR forecast methods. (author)
A new spinning reserve requirement forecast method for deregulated electricity markets
Energy Technology Data Exchange (ETDEWEB)
Amjady, Nima; Keynia, Farshid [Department of Electrical Engineering, Semnan University, Semnan (Iran)
2010-06-15
Ancillary services are necessary for maintaining the security and reliability of power systems and constitute an important part of trade in competitive electricity markets. Spinning Reserve (SR) is one of the most important ancillary services for saving power system stability and integrity in response to contingencies and disturbances that continuously occur in the power systems. Hence, an accurate day-ahead forecast of SR requirement helps the Independent System Operator (ISO) to conduct a reliable and economic operation of the power system. However, SR signal has complex, non-stationary and volatile behavior along the time domain and depends greatly on system load. In this paper, a new hybrid forecast engine is proposed for SR requirement prediction. The proposed forecast engine has an iterative training mechanism composed of Levenberg-Marquadt (LM) learning algorithm and Real Coded Genetic Algorithm (RCGA), implemented on the Multi-Layer Perceptron (MLP) neural network. The proposed forecast methodology is examined by means of real data of Pennsylvania-New Jersey-Maryland (PJM) electricity market and the California ISO (CAISO) controlled grid. The obtained forecast results are presented and compared with those of the other SR forecast methods. (author)
The essential theory of fast wave current drive with full wave method
International Nuclear Information System (INIS)
Liu Yan; Gong Xueyu; Yang Lei; Yin Chenyan; Yin Lan
2007-01-01
The full wave numerical method is developed for analyzing fast wave current drive in the range of ion cyclotron waves in tokamak plasmas, taking into account finite larmor radius effects and parallel dispersion. the physical model, the dispersion relation on the assumption of Finite Larmor Radius (FLR) effects and the form of full wave be used for computer simulation are developed. All of the work will contribute to further study of fast wave current drive. (authors)
Refined Source Terms in WAVEWATCH III with Wave Breaking and Sea Spray Forecasts
2015-09-30
dissipation and breaking, nonlinear wave-wave interaction, bottom friction, wave-mud interaction, wave-current interaction as well as sea spray flux. These...shallow water outside the surf zone. After careful testing within a comprehensive suite of test bed cases, these refined source terms will be...aim to refine the parameterization of air-sea and upper ocean fluxes, including wind input and sea spray as well as dissipation, and hence improve
Study on The Extended Range Weather Forecast of Low Frequency Signal Based on Period Analysis Method
Li, X.
2016-12-01
Although many studies have explored the MJO and its application for weather forecasting, low-frequency oscillation has been insufficiently studied for the extend range weather forecasting over middle and high latitudes. In China, low-frequency synoptic map is a useful tool for meteorological operation department to forecast extend range weather. It is therefore necessary to develop objective methods to serve the need for finding low-frequency signal, interpretation and application of this signal in the extend range weather forecasting. In this paper, method of Butterworth band pass filter was applied to get low-frequency height field at 500hPa from 1980 to 2014 by using NCEP/NCAR daily grid data. Then period analysis and optimal subset regression methods were used to process the low frequency data of 150 days before the first forecast day and extend the low frequency signal of 500hPa low-frequency high field to future 30 days in the global from June to August during 2011-2014. Finally, the results were test. The main results are as follows: (1) In general, the fitting effect of low frequency signals of 500hPa low-frequency height field by period analysis in the northern hemisphere was better than that in the southern hemisphere, and was better in the low latitudes than that in the high latitudes. The fitting accuracy gradually reduced with the increase of forecast time length, which tended to be stable during the late forecasting period. (2) The fitting effects over the 6 key regions in China showed that except filtering result over Xinjiang area in the first 10 days and 30 days, filtering results over the other 5 key regions throughout the whole period have passed reliability test with level more than 95%. (3) The center and scope of low and high low frequency systems can be fitted well by using the methods mentioned above, which is consist with the corresponding use of the low-frequency synoptic map for the prediction of the extended period. Application of the
Laugel, Amélie; Menendez, Melisa; Benoit, Michel; Mattarolo, Giovanni; Mendez, Fernando
2013-04-01
Wave climate forecasting is a major issue for numerous marine and coastal related activities, such as offshore industries, flooding risks assessment and wave energy resource evaluation, among others. Generally, there are two main ways to predict the impacts of the climate change on the wave climate at regional scale: the dynamical and the statistical downscaling of GCM (Global Climate Model). In this study, both methods have been applied on the French coast (Atlantic , English Channel and North Sea shoreline) under three climate change scenarios (A1B, A2, B1) simulated with the GCM ARPEGE-CLIMAT, from Météo-France (AR4, IPCC). The aim of the work is to characterise the wave climatology of the 21st century and compare the statistical and dynamical methods pointing out advantages and disadvantages of each approach. The statistical downscaling method proposed by the Environmental Hydraulics Institute of Cantabria (Spain) has been applied (Menendez et al., 2011). At a particular location, the sea-state climate (Predictand Y) is defined as a function, Y=f(X), of several atmospheric circulation patterns (Predictor X). Assuming these climate associations between predictor and predictand are stationary, the statistical approach has been used to project the future wave conditions with reference to the GCM. The statistical relations between predictor and predictand have been established over 31 years, from 1979 to 2009. The predictor is built as the 3-days-averaged squared sea level pressure gradient from the hourly CFSR database (Climate Forecast System Reanalysis, http://cfs.ncep.noaa.gov/cfsr/). The predictand has been extracted from the 31-years hindcast sea-state database ANEMOC-2 performed with the 3G spectral wave model TOMAWAC (Benoit et al., 1996), developed at EDF R&D LNHE and Saint-Venant Laboratory for Hydraulics and forced by the CFSR 10m wind field. Significant wave height, peak period and mean wave direction have been extracted with an hourly-resolution at
Zhang, Wei-Na; Huang, Hui-ming; Wang, Yi-gang; Chen, Da-ke; Zhang, lin
2018-03-01
Understanding the drifting motion of a small semi-submersible drifter is of vital importance regarding monitoring surface currents and the floating pollutants in coastal regions. This work addresses this issue by establishing a mechanistic drifting forecast model based on kinetic analysis. Taking tide-wind-wave into consideration, the forecast model is validated against in situ drifting experiment in the Radial Sand Ridges. Model results show good performance with respect to the measured drifting features, characterized by migrating back and forth twice a day with daily downwind displacements. Trajectory models are used to evaluate the influence of the individual hydrodynamic forcing. The tidal current is the fundamental dynamic condition in the Radial Sand Ridges and has the greatest impact on the drifting distance. However, it loses its leading position in the field of the daily displacement of the used drifter. The simulations reveal that different hydrodynamic forces dominate the daily displacement of the used drifter at different wind scales. The wave-induced mass transport has the greatest influence on the daily displacement at Beaufort wind scale 5-6; while wind drag contributes mostly at wind scale 2-4.
Development of K-Nearest Neighbour Regression Method in Forecasting River Stream Flow
Directory of Open Access Journals (Sweden)
Mohammad Azmi
2012-07-01
Full Text Available Different statistical, non-statistical and black-box methods have been used in forecasting processes. Among statistical methods, K-nearest neighbour non-parametric regression method (K-NN due to its natural simplicity and mathematical base is one of the recommended methods for forecasting processes. In this study, K-NN method is explained completely. Besides, development and improvement approaches such as best neighbour estimation, data transformation functions, distance functions and proposed extrapolation method are described. K-NN method in company with its development approaches is used in streamflow forecasting of Zayandeh-Rud Dam upper basin. Comparing between final results of classic K-NN method and modified K-NN (number of neighbour 5, transformation function of Range Scaling, distance function of Mahanalobis and proposed extrapolation method shows that modified K-NN in criteria of goodness of fit, root mean square error, percentage of volume of error and correlation has had performance improvement 45% , 59% and 17% respectively. These results approve necessity of applying mentioned approaches to derive more accurate forecasts.
Seo, Eunkyo; Lee, Myong-In; Jeong, Jee-Hoon; Koster, Randal D.; Schubert, Siegfried D.; Kim, Hye-Mi; Kim, Daehyun; Kang, Hyun-Suk; Kim, Hyun-Kyung; MacLachlan, Craig; Scaife, Adam A.
2018-05-01
This study uses a global land-atmosphere coupled model, the land-atmosphere component of the Global Seasonal Forecast System version 5, to quantify the degree to which soil moisture initialization could potentially enhance boreal summer surface air temperature forecast skill. Two sets of hindcast experiments are performed by prescribing the observed sea surface temperature as the boundary condition for a 15-year period (1996-2010). In one set of the hindcast experiments (noINIT), the initial soil moisture conditions are randomly taken from a long-term simulation. In the other set (INIT), the initial soil moisture conditions are taken from an observation-driven offline Land Surface Model (LSM) simulation. The soil moisture conditions from the offline LSM simulation are calibrated using the forecast model statistics to minimize the inconsistency between the LSM and the land-atmosphere coupled model in their mean and variability. Results show a higher boreal summer surface air temperature prediction skill in INIT than in noINIT, demonstrating the potential benefit from an accurate soil moisture initialization. The forecast skill enhancement appears especially in the areas in which the evaporative fraction—the ratio of surface latent heat flux to net surface incoming radiation—is sensitive to soil moisture amount. These areas lie in the transitional regime between humid and arid climates. Examination of the extreme 2003 European and 2010 Russian heat wave events reveal that the regionally anomalous soil moisture conditions during the events played an important role in maintaining the stationary circulation anomalies, especially those near the surface.
An Advanced Bayesian Method for Short-Term Probabilistic Forecasting of the Generation of Wind Power
Directory of Open Access Journals (Sweden)
Antonio Bracale
2015-09-01
Full Text Available Currently, among renewable distributed generation systems, wind generators are receiving a great deal of interest due to the great economic, technological, and environmental incentives they involve. However, the uncertainties due to the intermittent nature of wind energy make it difficult to operate electrical power systems optimally and make decisions that satisfy the needs of all the stakeholders of the electricity energy market. Thus, there is increasing interest determining how to forecast wind power production accurately. Most the methods that have been published in the relevant literature provided deterministic forecasts even though great interest has been focused recently on probabilistic forecast methods. In this paper, an advanced probabilistic method is proposed for short-term forecasting of wind power production. A mixture of two Weibull distributions was used as a probability function to model the uncertainties associated with wind speed. Then, a Bayesian inference approach with a particularly-effective, autoregressive, integrated, moving-average model was used to determine the parameters of the mixture Weibull distribution. Numerical applications also are presented to provide evidence of the forecasting performance of the Bayesian-based approach.
A ''quadratized'' augmented plane wave method
International Nuclear Information System (INIS)
Smrcka, L.
1982-02-01
The exact radial solution inside the muffin-tin sphere is replaced by its Taylor expansion with respect to the energy, truncated after the quadratic term. Making use of it the energy independent augmented plane waves are formed which lead to the secular equations linear in energy. The method resembles the currently used linearized APW method but yields higher accuracy. The analysis of solution inside one muffin-tin sphere shows that the eigenvalue error is proportional to (E-E 0 ) 6 as compared with (E-E 0 ) 4 for LAPW. The error of eigenfunctions is (E-E 0 ) 3 ((E-E 0 ) 2 for LAPW). These conclusions are confirmed by direct numerical calculation of band structure of Cu and Al. (author)
Theory Study and Application of the BP-ANN Method for Power Grid Short-Term Load Forecasting
Institute of Scientific and Technical Information of China (English)
Xia Hua; Gang Zhang; Jiawei Yang; Zhengyuan Li
2015-01-01
Aiming at the low accuracy problem of power system short⁃term load forecasting by traditional methods, a back⁃propagation artifi⁃cial neural network (BP⁃ANN) based method for short⁃term load forecasting is presented in this paper. The forecast points are re⁃lated to prophase adjacent data as well as the periodical long⁃term historical load data. Then the short⁃term load forecasting model of Shanxi Power Grid (China) based on BP⁃ANN method and correlation analysis is established. The simulation model matches well with practical power system load, indicating the BP⁃ANN method is simple and with higher precision and practicality.
International Nuclear Information System (INIS)
Zhao, W.; Baskaran, D.; Grishchuk, L. P.
2009-01-01
The relic gravitational waves are the cleanest probe of the violent times in the very early history of the Universe. They are expected to leave signatures in the observed cosmic microwave background anisotropies. We significantly improved our previous analysis [W. Zhao, D. Baskaran, and L. P. Grishchuk, Phys. Rev. D 79, 023002 (2009)] of the 5-year WMAP TT and TE data at lower multipoles l. This more general analysis returned essentially the same maximum likelihood result (unfortunately, surrounded by large remaining uncertainties): The relic gravitational waves are present and they are responsible for approximately 20% of the temperature quadrupole. We identify and discuss the reasons by which the contribution of gravitational waves can be overlooked in a data analysis. One of the reasons is a misleading reliance on data from very high multipoles l and another a too narrow understanding of the problem as the search for B modes of polarization, rather than the detection of relic gravitational waves with the help of all correlation functions. Our analysis of WMAP5 data has led to the identification of a whole family of models characterized by relatively high values of the likelihood function. Using the Fisher matrix formalism we formulated forecasts for Planck mission in the context of this family of models. We explore in detail various 'optimistic', 'pessimistic', and 'dream case' scenarios. We show that in some circumstances the B-mode detection may be very inconclusive, at the level of signal-to-noise ratio S/N=1.75, whereas a smarter data analysis can reveal the same gravitational wave signal at S/N=6.48. The final result is encouraging. Even under unfavorable conditions in terms of instrumental noises and foregrounds, the relic gravitational waves, if they are characterized by the maximum likelihood parameters that we found from WMAP5 data, will be detected by Planck at the level S/N=3.65.
Projector augmented wave method: ab initio molecular dynamics ...
Indian Academy of Sciences (India)
Unknown
kinetic energy is small and the wave function is smooth. However, the wave ... and various strategies have been developed. ... methods let us briefly review the history of augmented ..... alleviated by adding an intelligent zero: If an operator B.
Karpušenkaitė, Aistė; Ruzgas, Tomas; Denafas, Gintaras
2018-05-01
The aim of the study was to create a hybrid forecasting method that could produce higher accuracy forecasts than previously used 'pure' time series methods. Mentioned methods were already tested with total automotive waste, hazardous automotive waste, and total medical waste generation, but demonstrated at least a 6% error rate in different cases and efforts were made to decrease it even more. Newly developed hybrid models used a random start generation method to incorporate different time-series advantages and it helped to increase the accuracy of forecasts by 3%-4% in hazardous automotive waste and total medical waste generation cases; the new model did not increase the accuracy of total automotive waste generation forecasts. Developed models' abilities to forecast short- and mid-term forecasts were tested using prediction horizon.
Doos, Lucy; Packer, Claire; Ward, Derek; Simpson, Sue; Stevens, Andrew
2016-03-10
Forecasting can support rational decision-making around the introduction and use of emerging health technologies and prevent investment in technologies that have limited long-term potential. However, forecasting methods need to be credible. We performed a systematic search to identify the methods used in forecasting studies to predict future health technologies within a 3-20-year timeframe. Identification and retrospective assessment of such methods potentially offer a route to more reliable prediction. Systematic search of the literature to identify studies reported on methods of forecasting in healthcare. People are not needed in this study. The authors searched MEDLINE, EMBASE, PsychINFO and grey literature sources, and included articles published in English that reported their methods and a list of identified technologies. Studies reporting methods used to predict future health technologies within a 3-20-year timeframe with an identified list of individual healthcare technologies. Commercially sponsored reviews, long-term futurology studies (with over 20-year timeframes) and speculative editorials were excluded. 15 studies met our inclusion criteria. Our results showed that the majority of studies (13/15) consulted experts either alone or in combination with other methods such as literature searching. Only 2 studies used more complex forecasting tools such as scenario building. The methodological fundamentals of formal 3-20-year prediction are consistent but vary in details. Further research needs to be conducted to ascertain if the predictions made were accurate and whether accuracy varies by the methods used or by the types of technologies identified. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/
Energy Technology Data Exchange (ETDEWEB)
Kiseleva, N.N.; Burkhanov, G.S.
1988-05-01
New ABC compounds have been forecast as having structures of TiNiSi, ZrNiAl, MgAgAs and PbFCl types, while AB/sub 2/C/sub 2/ ones have structures of ThCr/sub 2/Si/sub 2/ and CaAl/sub 2/Si/sub 2/ (C = P, As, Sb, or Bi, while A and B are metals or semimetals). Only the fundamental properties of the elements are used. Cybernetic methods and computer training are effective in forecasting new crystalline phases.
Papacharalampous, Georgia; Tyralis, Hristos; Koutsoyiannis, Demetris
2018-02-01
We investigate the predictability of monthly temperature and precipitation by applying automatic univariate time series forecasting methods to a sample of 985 40-year-long monthly temperature and 1552 40-year-long monthly precipitation time series. The methods include a naïve one based on the monthly values of the last year, as well as the random walk (with drift), AutoRegressive Fractionally Integrated Moving Average (ARFIMA), exponential smoothing state-space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components (BATS), simple exponential smoothing, Theta and Prophet methods. Prophet is a recently introduced model inspired by the nature of time series forecasted at Facebook and has not been applied to hydrometeorological time series before, while the use of random walk, BATS, simple exponential smoothing and Theta is rare in hydrology. The methods are tested in performing multi-step ahead forecasts for the last 48 months of the data. We further investigate how different choices of handling the seasonality and non-normality affect the performance of the models. The results indicate that: (a) all the examined methods apart from the naïve and random walk ones are accurate enough to be used in long-term applications; (b) monthly temperature and precipitation can be forecasted to a level of accuracy which can barely be improved using other methods; (c) the externally applied classical seasonal decomposition results mostly in better forecasts compared to the automatic seasonal decomposition used by the BATS and Prophet methods; and (d) Prophet is competitive, especially when it is combined with externally applied classical seasonal decomposition.
Relativistic electrons of the outer radiation belt and methods of their forecast (review
Directory of Open Access Journals (Sweden)
Potapov A.S.
2017-03-01
Full Text Available The paper reviews studies of the dynamics of relativistic electrons in the geosynchronous region. It lists the physical processes that lead to the acceleration of electrons filling the outer radiation belt. As one of the space weather factors, high-energy electron fluxes pose a serious threat to the operation of satellite equipment in one of the most populated orbital regions. Necessity is emphasized for efforts to develop methods for forecasting the situation in this part of the magnetosphere, possible predictors are listed, and their classification is given. An example of a predictive model for forecasting relativistic electron flux with a 1–2-day lead time is proposed. Some questions of practical organization of prediction are discussed; the main objectives of short-term, medium-term, and long-term forecasts are listed.
Directory of Open Access Journals (Sweden)
A. Dörnbrack
2012-04-01
Full Text Available The relatively warm 2009–2010 Arctic winter was an exceptional one as the North Atlantic Oscillation index attained persistent extreme negative values. Here, selected aspects of the Arctic stratosphere during this winter inspired by the analysis of the international field experiment RECONCILE are presented. First of all, and as a kind of reference, the evolution of the polar vortex in its different phases is documented. Special emphasis is put on explaining the formation of the exceptionally cold vortex in mid winter after a sequence of stratospheric disturbances which were caused by upward propagating planetary waves. A major sudden stratospheric warming (SSW occurring near the end of January 2010 concluded the anomalous cold vortex period. Wave ice polar stratospheric clouds were frequently observed by spaceborne remote-sensing instruments over the Arctic during the cold period in January 2010. Here, one such case observed over Greenland is analysed in more detail and an attempt is made to correlate flow information of an operational numerical weather prediction model to the magnitude of the mountain-wave induced temperature fluctuations. Finally, it is shown that the forecasts of the ECMWF ensemble prediction system for the onset of the major SSW were very skilful and the ensemble spread was very small. However, the ensemble spread increased dramatically after the major SSW, displaying the strong non-linearity and internal variability involved in the SSW event.
Traveling wave tube and method of manufacture
Vancil, Bernard K. (Inventor)
2004-01-01
A traveling wave tube includes a glass or other insulating envelope having a plurality of substantially parallel glass rods supported therewithin which in turn support an electron gun, a collector and an intermediate slow wave structure. The slow wave structure itself provides electrostatic focussing of a central electron beam thereby eliminating the need for focussing magnetics and materially decreasing the cost of construction as well as enabling miniaturization. The slow wave structure advantageously includes cavities along the electron beam through which the r.f. energy is propagated, or a double, interleaved ring loop structure supported by dielectric fins within a ground plane cylinder disposed coaxially within the glass envelope.
An Evaluation of a High-Resolution Operational Wave Forecasting System in the Adriatic Sea
2009-01-01
1226 Office of Counsel,Code 1008.3 ADOR/Director NCST E. R. Franchi , 7000 Public Affairs (Unclassified/ Unlimited Only), Code 703o 4 VO-oV 4/2...examine and evaluate wind-wave modeling capability (Cavaleri et al., 1989). Many institutions run global and regional atmospheric and wave models...limited-area model (LAM) built on the basis of the global model IFS/ARPEGE (ARPEGE - Action de Recherche Petite Echelle Grande Echelle, 1FS - Integrated
International Nuclear Information System (INIS)
Azimi, R.; Ghayekhloo, M.; Ghofrani, M.
2016-01-01
Highlights: • A novel clustering approach is proposed based on the data transformation approach. • A novel cluster selection method based on correlation analysis is presented. • The proposed hybrid clustering approach leads to deep learning for MLPNN. • A hybrid forecasting method is developed to predict solar radiations. • The evaluation results show superior performance of the proposed forecasting model. - Abstract: Accurate forecasting of renewable energy sources plays a key role in their integration into the grid. This paper proposes a hybrid solar irradiance forecasting framework using a Transformation based K-means algorithm, named TB K-means, to increase the forecast accuracy. The proposed clustering method is a combination of a new initialization technique, K-means algorithm and a new gradual data transformation approach. Unlike the other K-means based clustering methods which are not capable of providing a fixed and definitive answer due to the selection of different cluster centroids for each run, the proposed clustering provides constant results for different runs of the algorithm. The proposed clustering is combined with a time-series analysis, a novel cluster selection algorithm and a multilayer perceptron neural network (MLPNN) to develop the hybrid solar radiation forecasting method for different time horizons (1 h ahead, 2 h ahead, …, 48 h ahead). The performance of the proposed TB K-means clustering is evaluated using several different datasets and compared with different variants of K-means algorithm. Solar datasets with different solar radiation characteristics are also used to determine the accuracy and processing speed of the developed forecasting method with the proposed TB K-means and other clustering techniques. The results of direct comparison with other well-established forecasting models demonstrate the superior performance of the proposed hybrid forecasting method. Furthermore, a comparative analysis with the benchmark solar
Directory of Open Access Journals (Sweden)
Wan Yang
2014-04-01
Full Text Available A variety of filtering methods enable the recursive estimation of system state variables and inference of model parameters. These methods have found application in a range of disciplines and settings, including engineering design and forecasting, and, over the last two decades, have been applied to infectious disease epidemiology. For any system of interest, the ideal filter depends on the nonlinearity and complexity of the model to which it is applied, the quality and abundance of observations being entrained, and the ultimate application (e.g. forecast, parameter estimation, etc.. Here, we compare the performance of six state-of-the-art filter methods when used to model and forecast influenza activity. Three particle filters--a basic particle filter (PF with resampling and regularization, maximum likelihood estimation via iterated filtering (MIF, and particle Markov chain Monte Carlo (pMCMC--and three ensemble filters--the ensemble Kalman filter (EnKF, the ensemble adjustment Kalman filter (EAKF, and the rank histogram filter (RHF--were used in conjunction with a humidity-forced susceptible-infectious-recovered-susceptible (SIRS model and weekly estimates of influenza incidence. The modeling frameworks, first validated with synthetic influenza epidemic data, were then applied to fit and retrospectively forecast the historical incidence time series of seven influenza epidemics during 2003-2012, for 115 cities in the United States. Results suggest that when using the SIRS model the ensemble filters and the basic PF are more capable of faithfully recreating historical influenza incidence time series, while the MIF and pMCMC do not perform as well for multimodal outbreaks. For forecast of the week with the highest influenza activity, the accuracies of the six model-filter frameworks are comparable; the three particle filters perform slightly better predicting peaks 1-5 weeks in the future; the ensemble filters are more accurate predicting peaks in
Forecasting of steel consumption with use of nearest neighbors method
Directory of Open Access Journals (Sweden)
Rogalewicz Michał
2017-01-01
Full Text Available In the process of building a steel construction, its design is usually commissioned to the design office. Then a quotation is made and the finished offer is delivered to the customer. Its final shape is influenced by steel consumption to a great extent. Correct determination of the potential consumption of this material most often determines the profitability of the project. Because of a long waiting time for a final project from the design office, it is worthwhile to pre-analyze the project’s profitability and feasibility using historical data on already realized orders. The paper presents an innovative approach to decision-making support in one of the Polish construction companies. The authors have defined and prioritized the most important factors that differentiate the executed orders and have the greatest impact on steel consumption. These are, among others: height and width of steel structure, number of aisles, type of roof, etc. Then they applied and adapted the method of k-nearest neighbors to the specificity of the discussed problem. The goal was to search a set of historical orders and find the most similar to the analyzed one. On this basis, consumption of steel can be estimated. The method was programmed within the EXPLOR application.
Mountain Wave Analysis Using Fourier Methods
National Research Council Canada - National Science Library
Roadcap, John R
2007-01-01
...) their requirements for only a coarse horizontal background state. Common traits of Fourier mountain wave models include use of the Boussinesq approximation and neglect of moisture and Coriolis terms...
Khan, Valentina; Tscepelev, Valery; Vilfand, Roman; Kulikova, Irina; Kruglova, Ekaterina; Tischenko, Vladimir
2016-04-01
Long-range forecasts at monthly-seasonal time scale are in great demand of socio-economic sectors for exploiting climate-related risks and opportunities. At the same time, the quality of long-range forecasts is not fully responding to user application necessities. Different approaches, including combination of different prognostic models, are used in forecast centers to increase the prediction skill for specific regions and globally. In the present study, two forecasting methods are considered which are exploited in operational practice of Hydrometeorological Center of Russia. One of them is synoptical-analogous method of forecasting of surface air temperature at monthly scale. Another one is dynamical system based on the global semi-Lagrangian model SL-AV, developed in collaboration of Institute of Numerical Mathematics and Hydrometeorological Centre of Russia. The seasonal version of this model has been used to issue global and regional forecasts at monthly-seasonal time scales. This study presents results of the evaluation of surface air temperature forecasts generated with using above mentioned synoptical-statistical and dynamical models, and their combination to potentially increase skill score over Northern Eurasia. The test sample of operational forecasts is encompassing period from 2010 through 2015. The seasonal and interannual variability of skill scores of these methods has been discussed. It was noticed that the quality of all forecasts is highly dependent on the inertia of macro-circulation processes. The skill scores of forecasts are decreasing during significant alterations of synoptical fields for both dynamical and empirical schemes. Procedure of combination of forecasts from different methods, in some cases, has demonstrated its effectiveness. For this study the support has been provided by Grant of Russian Science Foundation (№14-37-00053).
Taylor-series method for four-nucleon wave functions
International Nuclear Information System (INIS)
Sandulescu, A.; Tarnoveanu, I.; Rizea, M.
1977-09-01
Taylor-series method for transforming the infinite or finite well two-nucleon wave functions from individual coordinates to relative and c.m. coordinates, by expanding the single particle shell model wave functions around c.m. of the system, is generalized to four-nucleon wave functions. Also the connections with the Talmi-Moshinsky method for two and four harmonic oscillator wave functions are deduced. For both methods Fortran IV programs for the expansion coefficients have been written and the equivalence of corresponding expressions numerically proved. (author)
Directory of Open Access Journals (Sweden)
Hongzhuan Zhao
2016-04-01
Full Text Available With the enrichment of perception methods, modern transportation system has many physical objects whose states are influenced by many information factors so that it is a typical Cyber-Physical System (CPS. Thus, the traffic information is generally multi-sourced, heterogeneous and hierarchical. Existing research results show that the multisourced traffic information through accurate classification in the process of information fusion can achieve better parameters forecasting performance. For solving the problem of traffic information accurate classification, via analysing the characteristics of the multi-sourced traffic information and using redefined binary tree to overcome the shortcomings of the original Support Vector Machine (SVM classification in information fusion, a multi-classification method using improved SVM in information fusion for traffic parameters forecasting is proposed. The experiment was conducted to examine the performance of the proposed scheme, and the results reveal that the method can get more accurate and practical outcomes.
Comparative study of four time series methods in forecasting typhoid fever incidence in China.
Directory of Open Access Journals (Sweden)
Xingyu Zhang
Full Text Available Accurate incidence forecasting of infectious disease is critical for early prevention and for better government strategic planning. In this paper, we present a comprehensive study of different forecasting methods based on the monthly incidence of typhoid fever. The seasonal autoregressive integrated moving average (SARIMA model and three different models inspired by neural networks, namely, back propagation neural networks (BPNN, radial basis function neural networks (RBFNN, and Elman recurrent neural networks (ERNN were compared. The differences as well as the advantages and disadvantages, among the SARIMA model and the neural networks were summarized and discussed. The data obtained for 2005 to 2009 and for 2010 from the Chinese Center for Disease Control and Prevention were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE, mean absolute percentage error (MAPE, and mean square error (MSE. The results showed that RBFNN obtained the smallest MAE, MAPE and MSE in both the modeling and forecasting processes. The performances of the four models ranked in descending order were: RBFNN, ERNN, BPNN and the SARIMA model.
Comparative study of four time series methods in forecasting typhoid fever incidence in China.
Zhang, Xingyu; Liu, Yuanyuan; Yang, Min; Zhang, Tao; Young, Alistair A; Li, Xiaosong
2013-01-01
Accurate incidence forecasting of infectious disease is critical for early prevention and for better government strategic planning. In this paper, we present a comprehensive study of different forecasting methods based on the monthly incidence of typhoid fever. The seasonal autoregressive integrated moving average (SARIMA) model and three different models inspired by neural networks, namely, back propagation neural networks (BPNN), radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) were compared. The differences as well as the advantages and disadvantages, among the SARIMA model and the neural networks were summarized and discussed. The data obtained for 2005 to 2009 and for 2010 from the Chinese Center for Disease Control and Prevention were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The results showed that RBFNN obtained the smallest MAE, MAPE and MSE in both the modeling and forecasting processes. The performances of the four models ranked in descending order were: RBFNN, ERNN, BPNN and the SARIMA model.
Mid-term load forecasting of power systems by a new prediction method
International Nuclear Information System (INIS)
Amjady, Nima; Keynia, Farshid
2008-01-01
Mid-term load forecasting (MTLF) becomes an essential tool for today power systems, mainly in those countries whose power systems operate in a deregulated environment. Among different kinds of MTLF, this paper focuses on the prediction of daily peak load for one month ahead. This kind of load forecast has many applications like maintenance scheduling, mid-term hydro thermal coordination, adequacy assessment, management of limited energy units, negotiation of forward contracts, and development of cost efficient fuel purchasing strategies. However, daily peak load is a nonlinear, volatile, and nonstationary signal. Besides, lack of sufficient data usually further complicates this problem. The paper proposes a new methodology to solve it, composed of an efficient data model, preforecast mechanism and combination of neural network and evolutionary algorithm as the hybrid forecast technique. The proposed methodology is examined on the EUropean Network on Intelligent TEchnologies (EUNITE) test data and Iran's power system. We will also compare our strategy with the other MTLF methods revealing its capability to solve this load forecast problem
Short term load forecasting of anomalous load using hybrid soft computing methods
Rasyid, S. A.; Abdullah, A. G.; Mulyadi, Y.
2016-04-01
Load forecast accuracy will have an impact on the generation cost is more economical. The use of electrical energy by consumers on holiday, show the tendency of the load patterns are not identical, it is different from the pattern of the load on a normal day. It is then defined as a anomalous load. In this paper, the method of hybrid ANN-Particle Swarm proposed to improve the accuracy of anomalous load forecasting that often occur on holidays. The proposed methodology has been used to forecast the half-hourly electricity demand for power systems in the Indonesia National Electricity Market in West Java region. Experiments were conducted by testing various of learning rate and learning data input. Performance of this methodology will be validated with real data from the national of electricity company. The result of observations show that the proposed formula is very effective to short-term load forecasting in the case of anomalous load. Hybrid ANN-Swarm Particle relatively simple and easy as a analysis tool by engineers.
Liu, Li; Xu, Yue-Ping
2017-04-01
Ensemble flood forecasting driven by numerical weather prediction products is becoming more commonly used in operational flood forecasting applications.In this study, a hydrological ensemble flood forecasting system based on Variable Infiltration Capacity (VIC) model and quantitative precipitation forecasts from TIGGE dataset is constructed for Lanjiang Basin, Southeast China. The impacts of calibration strategies and ensemble methods on the performance of the system are then evaluated.The hydrological model is optimized by parallel programmed ɛ-NSGAII multi-objective algorithm and two respectively parameterized models are determined to simulate daily flows and peak flows coupled with a modular approach.The results indicatethat the ɛ-NSGAII algorithm permits more efficient optimization and rational determination on parameter setting.It is demonstrated that the multimodel ensemble streamflow mean have better skills than the best singlemodel ensemble mean (ECMWF) and the multimodel ensembles weighted on members and skill scores outperform other multimodel ensembles. For typical flood event, it is proved that the flood can be predicted 3-4 days in advance, but the flows in rising limb can be captured with only 1-2 days ahead due to the flash feature. With respect to peak flows selected by Peaks Over Threshold approach, the ensemble means from either singlemodel or multimodels are generally underestimated as the extreme values are smoothed out by ensemble process.
A wave propagation matrix method in semiclassical theory
International Nuclear Information System (INIS)
Lee, S.Y.; Takigawa, N.
1977-05-01
A wave propagation matrix method is used to derive the semiclassical formulae of the multiturning point problem. A phase shift matrix and a barrier transformation matrix are introduced to describe the processes of a particle travelling through a potential well and crossing a potential barrier respectively. The wave propagation matrix is given by the products of phase shift matrices and barrier transformation matrices. The method to study scattering by surface transparent potentials and the Bloch wave in solids is then applied
Long Term Solar Radiation Forecast Using Computational Intelligence Methods
Directory of Open Access Journals (Sweden)
João Paulo Coelho
2014-01-01
Full Text Available The point prediction quality is closely related to the model that explains the dynamic of the observed process. Sometimes the model can be obtained by simple algebraic equations but, in the majority of the physical systems, the relevant reality is too hard to model with simple ordinary differential or difference equations. This is the case of systems with nonlinear or nonstationary behaviour which require more complex models. The discrete time-series problem, obtained by sampling the solar radiation, can be framed in this type of situation. By observing the collected data it is possible to distinguish multiple regimes. Additionally, due to atmospheric disturbances such as clouds, the temporal structure between samples is complex and is best described by nonlinear models. This paper reports the solar radiation prediction by using hybrid model that combines support vector regression paradigm and Markov chains. The hybrid model performance is compared with the one obtained by using other methods like autoregressive (AR filters, Markov AR models, and artificial neural networks. The results obtained suggests an increasing prediction performance of the hybrid model regarding both the prediction error and dynamic behaviour.
An Interval Estimation Method of Patent Keyword Data for Sustainable Technology Forecasting
Directory of Open Access Journals (Sweden)
Daiho Uhm
2017-11-01
Full Text Available Technology forecasting (TF is forecasting the future state of a technology. It is exciting to know the future of technologies, because technology changes the way we live and enhances the quality of our lives. In particular, TF is an important area in the management of technology (MOT for R&D strategy and new product development. Consequently, there are many studies on TF. Patent analysis is one method of TF because patents contain substantial information regarding developed technology. The conventional methods of patent analysis are based on quantitative approaches such as statistics and machine learning. The most traditional TF methods based on patent analysis have a common problem. It is the sparsity of patent keyword data structured from collected patent documents. After preprocessing with text mining techniques, most frequencies of technological keywords in patent data have values of zero. This problem creates a disadvantage for the performance of TF, and we have trouble analyzing patent keyword data. To solve this problem, we propose an interval estimation method (IEM. Using an adjusted Wald confidence interval called the Agresti–Coull confidence interval, we construct our IEM for efficient TF. In addition, we apply the proposed method to forecast the technology of an innovative company. To show how our work can be applied in the real domain, we conduct a case study using Apple technology.
The use of satellite data assimilation methods in regional NWP for solar irradiance forecasting
Kurzrock, Frederik; Cros, Sylvain; Chane-Ming, Fabrice; Potthast, Roland; Linguet, Laurent; Sébastien, Nicolas
2016-04-01
As an intermittent energy source, the injection of solar power into electricity grids requires irradiance forecasting in order to ensure grid stability. On time scales of more than six hours ahead, numerical weather prediction (NWP) is recognized as the most appropriate solution. However, the current representation of clouds in NWP models is not sufficiently precise for an accurate forecast of solar irradiance at ground level. Dynamical downscaling does not necessarily increase the quality of irradiance forecasts. Furthermore, incorrectly simulated cloud evolution is often the cause of inaccurate atmospheric analyses. In non-interconnected tropical areas, the large amplitudes of solar irradiance variability provide abundant solar yield but present significant problems for grid safety. Irradiance forecasting is particularly important for solar power stakeholders in these regions where PV electricity penetration is increasing. At the same time, NWP is markedly more challenging in tropic areas than in mid-latitudes due to the special characteristics of tropical homogeneous convective air masses. Numerous data assimilation methods and strategies have evolved and been applied to a large variety of global and regional NWP models in the recent decades. Assimilating data from geostationary meteorological satellites is an appropriate approach. Indeed, models converting radiances measured by satellites into cloud properties already exist. Moreover, data are available at high temporal frequencies, which enable a pertinent cloud cover evolution modelling for solar energy forecasts. In this work, we present a survey of different approaches which aim at improving cloud cover forecasts using the assimilation of geostationary meteorological satellite data into regional NWP models. Various approaches have been applied to a variety of models and satellites and in different regions of the world. Current methods focus on the assimilation of cloud-top information, derived from infrared
expansion method and travelling wave solutions for the perturbed ...
Indian Academy of Sciences (India)
Abstract. In this paper, we construct the travelling wave solutions to the perturbed nonlinear. Schrödinger's equation (NLSE) with Kerr law non-linearity by the extended (G /G)-expansion method. Based on this method, we obtain abundant exact travelling wave solutions of NLSE with. Kerr law nonlinearity with arbitrary ...
The extended (G/G)-expansion method and travelling wave ...
Indian Academy of Sciences (India)
In this paper, we construct the travelling wave solutions to the perturbed nonlinear Schrödinger's equation (NLSE) with Kerr law non-linearity by the extended (′/)-expansion method. Based on this method, we obtain abundant exact travelling wave solutions of NLSE with Kerr law nonlinearity with arbitrary parameters.
Directory of Open Access Journals (Sweden)
Eimecke Jörgen
2017-09-01
Full Text Available Multistage expert surveys like the Delphi method are proven concepts for technology forecasting that enable the prediction of content-related and temporal development in fields of innovation (e.g., [1, 2]. Advantages of these qualitative multistage methods are a simple and easy to understand concept while still delivering valid results [3]. Nevertheless, the literature also points out certain disadvantages especially in large-scale technology forecasts in particularly abstract fields of innovation [4]. The proposed approach highlights the usefulness of the repertory grid method as an alternative for technology forecasting and as a first step for preference measurement. The basic approach from Baier and Kohler [5] is modified in-so-far that an online survey reduces the cognitive burden for the experts and simplifies the data collection process. Advantages over alternative approaches through its simple structure and through combining qualitative and quantitative methods are shown and an adaption on an actual field of innovation – civil drones in Germany – is done. The measurement of a common terminology for all experts minimizes misunderstandings during the interview and the achievement of an inter-individual comparable level of abstraction is forced by the laddering technique [6] during the interview.
International Nuclear Information System (INIS)
Tang, Pingzhou; Chen, Di; Hou, Yushuo
2016-01-01
As the world’s energy problem becomes more severe day by day, photovoltaic power generation has opened a new door for us with no doubt. It will provide an effective solution for this severe energy problem and meet human’s needs for energy if we can apply photovoltaic power generation in real life, Similar to wind power generation, photovoltaic power generation is uncertain. Therefore, the forecast of photovoltaic power generation is very crucial. In this paper, entropy method and extreme learning machine (ELM) method were combined to forecast a short-term photovoltaic power generation. First, entropy method is used to process initial data, train the network through the data after unification, and then forecast electricity generation. Finally, the data results obtained through the entropy method with ELM were compared with that generated through generalized regression neural network (GRNN) and radial basis function neural network (RBF) method. We found that entropy method combining with ELM method possesses higher accuracy and the calculation is faster.
Method and apparatus for generating acoustic waves
International Nuclear Information System (INIS)
Rao, G.V.; Gopal, R.
1982-01-01
A portable source of acoustic waves comprises a sample of iron-nickel alloy including an austenite phase cooled to become martensite, and a wave guide to transmit the acoustic waves. The source is applied to the pressure boundary region of a pressurized water reactor to simulate an actual metal flaw and test the calibration of the monitoring and surveillance system. With at most 29.7% nickel in the sample, the source provides acoustic emission due to ductile deformation, and with at least 30% nickel the acoustic emission is characteristic of a brittle deformation. Thus, the monitoring and surveillance system can be tested in either or both situations. In the prior art, synthetic waveform signals were used for such calibration but found not suitable for on-line simulation of the surveillance system. This invention provides an improved system in that it generates true acoustic signals. (author)
Parabolic approximation method for fast magnetosonic wave propagation in tokamaks
International Nuclear Information System (INIS)
Phillips, C.K.; Perkins, F.W.; Hwang, D.Q.
1985-07-01
Fast magnetosonic wave propagation in a cylindrical tokamak model is studied using a parabolic approximation method in which poloidal variations of the wave field are considered weak in comparison to the radial variations. Diffraction effects, which are ignored by ray tracing mthods, are included self-consistently using the parabolic method since continuous representations for the wave electromagnetic fields are computed directly. Numerical results are presented which illustrate the cylindrical convergence of the launched waves into a diffraction-limited focal spot on the cyclotron absorption layer near the magnetic axis for a wide range of plasma confinement parameters
Problems of the orthogonalized plane wave method. 1
International Nuclear Information System (INIS)
Farberovich, O.V.; Kurganskii, S.I.; Domashevskaya, E.P.
1979-01-01
The main problems of the orthogonalized plane wave method are discussed including (a) consideration of core states; (b) effect of overlap of wave functions of external core states upon the band structure; (c) calculation of d-type states. The modified orthogonal plane wave method (MOPW method) of Deegan and Twose is applied in a general form to solve the problems of the usual OPW method. For the first time the influence on the spectrum of the main parameters of the MOPW method is studied systematically by calculating the electronic energy spectrum in the transition metals Nb and V. (author)
D6.2–Load and generation forecasting methods and prototypes
DEFF Research Database (Denmark)
Madsen, Per Printz; Dueñas, Lara Pérez; Moraga, Carlos Castaño
. Most existing suppliers use anyway some kind of statistical approach to make the energy prediction. In the market there are few but strong providers of such services, and it has been preferred to use an external provider rather than developing ENCOURAGE’s own energy production algorithm. The external...... service chosen belongs to one of the partners of the consortium (Gnarum), so tests have been carried on to adapt the forecasting methods to the distributed small-scale generation case, with satisfactory results....
The System of Inventory Forecasting in PT. XYZ by using the Method of Holt Winter Multiplicative
Shaleh, W.; Rasim; Wahyudin
2018-01-01
Problems at PT. XYZ currently only rely on manual bookkeeping, then the cost of production will swell and all investments invested to be less to predict sales and inventory of goods. If the inventory prediction of goods is to large, then the cost of production will swell and all investments invested to be less efficient. Vice versa, if the inventory prediction is too small it will impact on consumers, so that consumers are forced to wait for the desired product. Therefore, in this era of globalization, the development of computer technology has become a very important part in every business plan. Almost of all companies, both large and small, use computer technology. By utilizing computer technology, people can make time in solving complex business problems. Computer technology for companies has become an indispensable activity to provide enhancements to the business services they manage but systems and technologies are not limited to the distribution model and data processing but the existing system must be able to analyze the possibilities of future company capabilities. Therefore, the company must be able to forecast conditions and circumstances, either from inventory of goods, force, or profits to be obtained. To forecast it, the data of total sales from December 2014 to December 2016 will be calculated by using the method of Holt Winters, which is the method of time series prediction (Multiplicative Seasonal Method) it is seasonal data that has increased and decreased, also has 4 equations i.e. Single Smoothing, Trending Smoothing, Seasonal Smoothing and Forecasting. From the results of research conducted, error value in the form of MAPE is below 1%, so it can be concluded that forecasting with the method of Holt Winter Multiplicative.
Directory of Open Access Journals (Sweden)
Han Lin Shang
2011-07-01
Full Text Available Using the age- and sex-specific data of 14 developed countries, we compare the point and interval forecast accuracy and bias of ten principal component methods for forecasting mortality rates and life expectancy. The ten methods are variants and extensions of the Lee-Carter method. Based on one-step forecast errors, the weighted Hyndman-Ullah method provides the most accurate point forecasts of mortality rates and the Lee-Miller method is the least biased. For the accuracy and bias of life expectancy, the weighted Hyndman-Ullah method performs the best for female mortality and the Lee-Miller method for male mortality. While all methods underestimate variability in mortality rates, the more complex Hyndman-Ullah methods are more accurate than the simpler methods. The weighted Hyndman-Ullah method provides the most accurate interval forecasts for mortality rates, while the robust Hyndman-Ullah method provides the best interval forecast accuracy for life expectancy.
Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data
Directory of Open Access Journals (Sweden)
Wei-Kuang Lai
2016-02-01
Full Text Available Traffic information estimation and forecasting methods based on cellular floating vehicle data (CFVD are proposed to analyze the signals (e.g., handovers (HOs, call arrivals (CAs, normal location updates (NLUs and periodic location updates (PLUs from cellular networks. For traffic information estimation, analytic models are proposed to estimate the traffic flow in accordance with the amounts of HOs and NLUs and to estimate the traffic density in accordance with the amounts of CAs and PLUs. Then, the vehicle speeds can be estimated in accordance with the estimated traffic flows and estimated traffic densities. For vehicle speed forecasting, a back-propagation neural network algorithm is considered to predict the future vehicle speed in accordance with the current traffic information (i.e., the estimated vehicle speeds from CFVD. In the experimental environment, this study adopted the practical traffic information (i.e., traffic flow and vehicle speed from Taiwan Area National Freeway Bureau as the input characteristics of the traffic simulation program and referred to the mobile station (MS communication behaviors from Chunghwa Telecom to simulate the traffic information and communication records. The experimental results illustrated that the average accuracy of the vehicle speed forecasting method is 95.72%. Therefore, the proposed methods based on CFVD are suitable for an intelligent transportation system.
Forecasting VaR and ES of stock index portfolio: A Vine copula method
Zhang, Bangzheng; Wei, Yu; Yu, Jiang; Lai, Xiaodong; Peng, Zhenfeng
2014-12-01
Risk measurement has both theoretical and practical significance in risk management. Using daily sample of 10 international stock indices, firstly this paper models the internal structures among different stock markets with C-Vine, D-Vine and R-Vine copula models. Secondly, the Value-at-Risk (VaR) and Expected Shortfall (ES) of the international stock markets portfolio are forecasted using Monte Carlo method based on the estimated dependence of different Vine copulas. Finally, the accuracy of VaR and ES measurements obtained from different statistical models are evaluated by UC, IND, CC and Posterior analysis. The empirical results show that the VaR forecasts at the quantile levels of 0.9, 0.95, 0.975 and 0.99 with three kinds of Vine copula models are sufficiently accurate. Several traditional methods, such as historical simulation, mean-variance and DCC-GARCH models, fail to pass the CC backtesting. The Vine copula methods can accurately forecast the ES of the portfolio on the base of VaR measurement, and D-Vine copula model is superior to other Vine copulas.
Quilty, J.; Adamowski, J. F.
2015-12-01
Urban water supply systems are often stressed during seasonal outdoor water use as water demands related to the climate are variable in nature making it difficult to optimize the operation of the water supply system. Urban water demand forecasts (UWD) failing to include meteorological conditions as inputs to the forecast model may produce poor forecasts as they cannot account for the increase/decrease in demand related to meteorological conditions. Meteorological records stochastically simulated into the future can be used as inputs to data-driven UWD forecasts generally resulting in improved forecast accuracy. This study aims to produce data-driven UWD forecasts for two different Canadian water utilities (Montreal and Victoria) using machine learning methods by first selecting historical UWD and meteorological records derived from a stochastic weather generator using nonlinear input variable selection. The nonlinear input variable selection methods considered in this work are derived from the concept of conditional mutual information, a nonlinear dependency measure based on (multivariate) probability density functions and accounts for relevancy, conditional relevancy, and redundancy from a potential set of input variables. The results of our study indicate that stochastic weather inputs can improve UWD forecast accuracy for the two sites considered in this work. Nonlinear input variable selection is suggested as a means to identify which meteorological conditions should be utilized in the forecast.
Directory of Open Access Journals (Sweden)
Petr Maca
2014-01-01
Full Text Available The presented paper aims to analyze the influence of the selection of transfer function and training algorithms on neural network flood runoff forecast. Nine of the most significant flood events, caused by the extreme rainfall, were selected from 10 years of measurement on small headwater catchment in the Czech Republic, and flood runoff forecast was investigated using the extensive set of multilayer perceptrons with one hidden layer of neurons. The analyzed artificial neural network models with 11 different activation functions in hidden layer were trained using 7 local optimization algorithms. The results show that the Levenberg-Marquardt algorithm was superior compared to the remaining tested local optimization methods. When comparing the 11 nonlinear transfer functions, used in hidden layer neurons, the RootSig function was superior compared to the rest of analyzed activation functions.
Directory of Open Access Journals (Sweden)
Zhaoxuan Li
2016-01-01
Full Text Available We evaluate and compare two common methods, artificial neural networks (ANN and support vector regression (SVR, for predicting energy productions from a solar photovoltaic (PV system in Florida 15 min, 1 h and 24 h ahead of time. A hierarchical approach is proposed based on the machine learning algorithms tested. The production data used in this work corresponds to 15 min averaged power measurements collected from 2014. The accuracy of the model is determined using computing error statistics such as mean bias error (MBE, mean absolute error (MAE, root mean square error (RMSE, relative MBE (rMBE, mean percentage error (MPE and relative RMSE (rRMSE. This work provides findings on how forecasts from individual inverters will improve the total solar power generation forecast of the PV system.
Forecast Method of Solar Irradiance with Just-In-Time Modeling
Suzuki, Takanobu; Goto, Yusuke; Terazono, Takahiro; Wakao, Shinji; Oozeki, Takashi
PV power output mainly depends on the solar irradiance which is affected by various meteorological factors. So, it is required to predict solar irradiance in the future for the efficient operation of PV systems. In this paper, we develop a novel approach for solar irradiance forecast, in which we introduce to combine the black-box model (JIT Modeling) with the physical model (GPV data). We investigate the predictive accuracy of solar irradiance over wide controlled-area of each electric power company by utilizing the measured data on the 44 observation points throughout Japan offered by JMA and the 64 points around Kanto by NEDO. Finally, we propose the application forecast method of solar irradiance to the point which is difficulty in compiling the database. And we consider the influence of different GPV default time on solar irradiance prediction.
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Florin POPESCU
2017-12-01
Full Text Available Early warning system (EWS based on a reliable forecasting process has become a critical component of the management of large complex industrial projects in the globalized transnational environment. The purpose of this research is to critically analyze the forecasting methods from the point of view of early warning, choosing those useful for the construction of EWS. This research addresses complementary techniques, using Bayesian Networks, which addresses both uncertainties and causality in project planning and execution, with the goal of generating early warning signals for project managers. Even though Bayesian networks have been widely used in a range of decision-support applications, their application as early warning systems for project management is still new.
Renko, Tanja; Ivušić, Sarah; Telišman Prtenjak, Maja; Šoljan, Vinko; Horvat, Igor
2018-03-01
In this study, a synoptic and mesoscale analysis was performed and Szilagyi's waterspout forecasting method was tested on ten waterspout events in the period of 2013-2016. Data regarding waterspout occurrences were collected from weather stations, an online survey at the official website of the National Meteorological and Hydrological Service of Croatia and eyewitness reports from newspapers and the internet. Synoptic weather conditions were analyzed using surface pressure fields, 500 hPa level synoptic charts, SYNOP reports and atmospheric soundings. For all observed waterspout events, a synoptic type was determined using the 500 hPa geopotential height chart. The occurrence of lightning activity was determined from the LINET lightning database, and waterspouts were divided into thunderstorm-related and "fair weather" ones. Mesoscale characteristics (with a focus on thermodynamic instability indices) were determined using the high-resolution (500 m grid length) mesoscale numerical weather model and model results were compared with the available observations. Because thermodynamic instability indices are usually insufficient for forecasting waterspout activity, the performance of the Szilagyi Waterspout Index (SWI) was tested using vertical atmospheric profiles provided by the mesoscale numerical model. The SWI successfully forecasted all waterspout events, even the winter events. This indicates that the Szilagyi's waterspout prognostic method could be used as a valid prognostic tool for the eastern Adriatic.
Prastuti, M.; Suhartono; Salehah, NA
2018-04-01
The need for energy supply, especially for electricity in Indonesia has been increasing in the last past years. Furthermore, the high electricity usage by people at different times leads to the occurrence of heteroscedasticity issue. Estimate the electricity supply that could fulfilled the community’s need is very important, but the heteroscedasticity issue often made electricity forecasting hard to be done. An accurate forecast of electricity consumptions is one of the key challenges for energy provider to make better resources and service planning and also take control actions in order to balance the electricity supply and demand for community. In this paper, hybrid ARIMAX Quantile Regression (ARIMAX-QR) approach was proposed to predict the short-term electricity consumption in East Java. This method will also be compared to time series regression using RMSE, MAPE, and MdAPE criteria. The data used in this research was the electricity consumption per half-an-hour data during the period of September 2015 to April 2016. The results show that the proposed approach can be a competitive alternative to forecast short-term electricity in East Java. ARIMAX-QR using lag values and dummy variables as predictors yield more accurate prediction in both in-sample and out-sample data. Moreover, both time series regression and ARIMAX-QR methods with addition of lag values as predictor could capture accurately the patterns in the data. Hence, it produces better predictions compared to the models that not use additional lag variables.
Predicting the Evolution of CO2 Emissions in Bahrain with Automated Forecasting Methods
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Cristiana Tudor
2016-09-01
Full Text Available The 2012 Doha meeting established the continuation of the Kyoto protocol, the legally-binding global agreement under which signatory countries had agreed to reduce their carbon emissions. Contrary to this assumed obligation, all G20 countries with the exception of France and the UK saw significant increases in their CO2 emissions over the last 25 years, surpassing 300% in the case of China. This paper attempts to forecast the evolution of carbon dioxide emissions in Bahrain over the 2012–2021 decade by employing seven Automated Forecasting Methods, including the exponential smoothing state space model (ETS, the Holt–Winters Model, the BATS/TBATS model, ARIMA, the structural time series model (STS, the naive model, and the neural network time series forecasting method (NNAR. Results indicate a reversal of the current decreasing trend of pollution in the country, with a point estimate of 2309 metric tons per capita at the end of 2020 and 2317 at the end of 2021, as compared to the 1934 level achieved in 2010. The country’s baseline level corresponding to year 1990 (as specified by the Doha amendment of the Kyoto protocol is approximately 25.54 metric tons per capita, which implies a maximum level of 20.96 metric tons per capita for the year 2020 (corresponding to a decrease of 18% relative to the baseline level in order for Bahrain to comply with the protocol. Our results therefore suggest that Bahrain cannot meet its assumed target.
Methods and tools to support real time risk-based flood forecasting - a UK pilot application
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Brown Emma
2016-01-01
Full Text Available Flood managers have traditionally used probabilistic models to assess potential flood risk for strategic planning and non-operational applications. Computational restrictions on data volumes and simulation times have meant that information on the risk of flooding has not been available for operational flood forecasting purposes. In practice, however, the operational flood manager has probabilistic questions to answer, which are not completely supported by the outputs of traditional, deterministic flood forecasting systems. In a collaborative approach, HR Wallingford and Deltares have developed methods, tools and techniques to extend existing flood forecasting systems with elements of strategic flood risk analysis, including probabilistic failure analysis, two dimensional flood spreading simulation and the analysis of flood impacts and consequences. This paper presents the results of the application of these new operational flood risk management tools to a pilot catchment in the UK. It discusses the problems of performing probabilistic flood risk assessment in real time and how these have been addressed in this study. It also describes the challenges of the communication of risk to operational flood managers and to the general public, and how these new methods and tools can provide risk-based supporting evidence to assist with this process.
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Xuejun Chen
2014-01-01
Full Text Available As one of the most promising renewable resources in electricity generation, wind energy is acknowledged for its significant environmental contributions and economic competitiveness. Because wind fluctuates with strong variation, it is quite difficult to describe the characteristics of wind or to estimate the power output that will be injected into the grid. In particular, short-term wind speed forecasting, an essential support for the regulatory actions and short-term load dispatching planning during the operation of wind farms, is currently regarded as one of the most difficult problems to be solved. This paper contributes to short-term wind speed forecasting by developing two three-stage hybrid approaches; both are combinations of the five-three-Hanning (53H weighted average smoothing method, ensemble empirical mode decomposition (EEMD algorithm, and nonlinear autoregressive (NAR neural networks. The chosen datasets are ten-minute wind speed observations, including twelve samples, and our simulation indicates that the proposed methods perform much better than the traditional ones when addressing short-term wind speed forecasting problems.
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Yuyang Gao
2016-09-01
Full Text Available With increasing importance being attached to big data mining, analysis, and forecasting in the field of wind energy, how to select an optimization model to improve the forecasting accuracy of the wind speed time series is not only an extremely challenging problem, but also a problem of concern for economic forecasting. The artificial intelligence model is widely used in forecasting and data processing, but the individual back-propagation artificial neural network cannot always satisfy the time series forecasting needs. Thus, a hybrid forecasting approach has been proposed in this study, which consists of data preprocessing, parameter optimization and a neural network for advancing the accuracy of short-term wind speed forecasting. According to the case study, in which the data are collected from Peng Lai, a city located in China, the simulation results indicate that the hybrid forecasting method yields better predictions compared to the individual BP, which indicates that the hybrid method exhibits stronger forecasting ability.
Influence of Wind Model Performance on Wave Forecasts of the Naval Oceanographic Office
Gay, P. S.; Edwards, K. L.
2017-12-01
Significant discrepancies between the Naval Oceanographic Office's significant wave height (SWH) predictions and observations have been noted in some model domains. The goal of this study is to evaluate these discrepancies and identify to what extent inaccuracies in the wind predictions may explain inaccuracies in SWH predictions. A one-year time series of data is evaluated at various locations in Southern California and eastern Florida. Correlations are generally quite good, ranging from 73% at Pendleton to 88% at both Santa Barbara, California, and Cape Canaveral, Florida. Correlations for month-long periods off Southern California drop off significantly in late spring through early autumn - less so off eastern Florida - likely due to weaker local wind seas and generally smaller SWH in addition to the influence of remotely-generated swell, which may not propagate accurately into and through the wave models. The results of this study suggest that it is likely that a change in meteorological and/or oceanographic conditions explains the change in model performance, partially as a result of a seasonal reduction in wind model performance in the summer months.
Manzanas, R.; Lucero, A.; Weisheimer, A.; Gutiérrez, J. M.
2018-02-01
Statistical downscaling methods are popular post-processing tools which are widely used in many sectors to adapt the coarse-resolution biased outputs from global climate simulations to the regional-to-local scale typically required by users. They range from simple and pragmatic Bias Correction (BC) methods, which directly adjust the model outputs of interest (e.g. precipitation) according to the available local observations, to more complex Perfect Prognosis (PP) ones, which indirectly derive local predictions (e.g. precipitation) from appropriate upper-air large-scale model variables (predictors). Statistical downscaling methods have been extensively used and critically assessed in climate change applications; however, their advantages and limitations in seasonal forecasting are not well understood yet. In particular, a key problem in this context is whether they serve to improve the forecast quality/skill of raw model outputs beyond the adjustment of their systematic biases. In this paper we analyze this issue by applying two state-of-the-art BC and two PP methods to downscale precipitation from a multimodel seasonal hindcast in a challenging tropical region, the Philippines. To properly assess the potential added value beyond the reduction of model biases, we consider two validation scores which are not sensitive to changes in the mean (correlation and reliability categories). Our results show that, whereas BC methods maintain or worsen the skill of the raw model forecasts, PP methods can yield significant skill improvement (worsening) in cases for which the large-scale predictor variables considered are better (worse) predicted by the model than precipitation. For instance, PP methods are found to increase (decrease) model reliability in nearly 40% of the stations considered in boreal summer (autumn). Therefore, the choice of a convenient downscaling approach (either BC or PP) depends on the region and the season.
A fast method for linear waves based on geometrical optics
Stolk, C.C.
2009-01-01
We develop a fast method for solving the one-dimensional wave equation based on geometrical optics. From geometrical optics (e.g., Fourier integral operator theory or WKB approximation) it is known that high-frequency waves split into forward and backward propagating parts, each propagating with the
A Case Study on a Combination NDVI Forecasting Model Based on the Entropy Weight Method
Energy Technology Data Exchange (ETDEWEB)
Huang, Shengzhi; Ming, Bo; Huang, Qiang; Leng, Guoyong; Hou, Beibei
2017-05-05
It is critically meaningful to accurately predict NDVI (Normalized Difference Vegetation Index), which helps guide regional ecological remediation and environmental managements. In this study, a combination forecasting model (CFM) was proposed to improve the performance of NDVI predictions in the Yellow River Basin (YRB) based on three individual forecasting models, i.e., the Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and Support Vector Machine (SVM) models. The entropy weight method was employed to determine the weight coefficient for each individual model depending on its predictive performance. Results showed that: (1) ANN exhibits the highest fitting capability among the four orecasting models in the calibration period, whilst its generalization ability becomes weak in the validation period; MLR has a poor performance in both calibration and validation periods; the predicted results of CFM in the calibration period have the highest stability; (2) CFM generally outperforms all individual models in the validation period, and can improve the reliability and stability of predicted results through combining the strengths while reducing the weaknesses of individual models; (3) the performances of all forecasting models are better in dense vegetation areas than in sparse vegetation areas.
Numerical method for wave forces acting on partially perforated caisson
Jiang, Feng; Tang, Xiao-cheng; Jin, Zhao; Zhang, Li; Chen, Hong-zhou
2015-04-01
The perforated caisson is widely applied to practical engineering because of its great advantages in effectively wave energy consumption and cost reduction. The attentions of many scientists were paid to the fluid-structure interaction between wave and perforated caisson studies, but until now, most concerns have been put on theoretical analysis and experimental model set up. In this paper, interaction between the wave and the partial perforated caisson in a 2D numerical wave flume is investigated by means of the renewed SPH algorithm, and the mathematical equations are in the form of SPH numerical approximation based on Navier-Stokes equations. The validity of the SPH mathematical method is examined and the simulated results are compared with the results of theoretical models, meanwhile the complex hydrodynamic characteristics when the water particles flow in or out of a wave absorbing chamber are analyzed and the wave pressure distribution of the perforated caisson is also addressed here. The relationship between the ratio of total horizontal force acting on caisson under regular waves and its influence factors is examined. The data show that the numerical calculation of the ratio of total horizontal force meets the empirical regression equation very well. The simulations of SPH about the wave nonlinearity and breaking are briefly depicted in the paper, suggesting that the advantages and great potentiality of the SPH method is significant compared with traditional methods.
Analysis shear wave velocity structure obtained from surface wave methods in Bornova, Izmir
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Pamuk, Eren, E-mail: eren.pamuk@deu.edu.tr; Akgün, Mustafa, E-mail: mustafa.akgun@deu.edu.tr [Department of Geophysical Engineering, Dokuz Eylul University, Izmir (Turkey); Özdağ, Özkan Cevdet, E-mail: cevdet.ozdag@deu.edu.tr [Dokuz Eylul University Rectorate, Izmir (Turkey)
2016-04-18
Properties of the soil from the bedrock is necessary to describe accurately and reliably for the reduction of earthquake damage. Because seismic waves change their amplitude and frequency content owing to acoustic impedance difference between soil and bedrock. Firstly, shear wave velocity and depth information of layers on bedrock is needed to detect this changing. Shear wave velocity can be obtained using inversion of Rayleigh wave dispersion curves obtained from surface wave methods (MASW- the Multichannel Analysis of Surface Waves, ReMi-Refraction Microtremor, SPAC-Spatial Autocorrelation). While research depth is limeted in active source study, a passive source methods are utilized for deep depth which is not reached using active source methods. ReMi method is used to determine layer thickness and velocity up to 100 m using seismic refraction measurement systems.The research carried out up to desired depth depending on radius using SPAC which is utilized easily in conditions that district using of seismic studies in the city. Vs profiles which are required to calculate deformations in under static and dynamic loads can be obtained with high resolution using combining rayleigh wave dispersion curve obtained from active and passive source methods. In the this study, Surface waves data were collected using the measurements of MASW, ReMi and SPAC at the İzmir Bornova region. Dispersion curves obtained from surface wave methods were combined in wide frequency band and Vs-depth profiles were obtained using inversion. Reliability of the resulting soil profiles were provided by comparison with theoretical transfer function obtained from soil paremeters and observed soil transfer function from Nakamura technique and by examination of fitting between these functions. Vs values are changed between 200-830 m/s and engineering bedrock (Vs>760 m/s) depth is approximately 150 m.
Mathematical Methods in Wave Propagation: Part 2--Non-Linear Wave Front Analysis
Jeffrey, Alan
1971-01-01
The paper presents applications and methods of analysis for non-linear hyperbolic partial differential equations. The paper is concluded by an account of wave front analysis as applied to the piston problem of gas dynamics. (JG)
Validation of Standing Wave Liner Impedance Measurement Method, Phase I
National Aeronautics and Space Administration — Hersh Acoustical Engineering, Inc. proposes to establish the feasibility and practicality of using the Standing Wave Method (SWM) to measure the impedance of...
Directory of Open Access Journals (Sweden)
Zhanglin Peng
2015-04-01
Full Text Available Purpose: Electric vehicles industry has gotten a rapid development in the world, especially in the developed countries, but still has a gap among different countries or regions. The advanced industrialization experiences of the EVs in the developed countries will have a great helpful for the development of EVs industrialization in the developing countries. This paper seeks to research the industrialization path & prospect of American EVs by forecasting electric vehicles demand and its proportion to the whole car sales based on the historical 37 EVs monthly sales and Cars monthly sales spanning from Dec. 2010 to Dec. 2013, and find out the key measurements to help Chinese government and automobile enterprises to promote Chinese EVs industrialization. Design/methodology: Compared with Single Exponential Smoothing method and Double Exponential Smoothing method, Triple exponential smoothing method is improved and applied in this study. Findings: The research results show that: American EVs industry will keep a sustained growth in the next 3 months. Price of the EVs, price of fossil oil, number of charging station, EVs technology and the government market & taxation polices have a different influence to EVs sales. So EVs manufacturers and policy-makers can adjust or reformulate some technology tactics and market measurements according to the forecast results. China can learn from American EVs polices and measurements to develop Chinese EVs industry. Originality/value: The main contribution of this paper is to use the triple exponential smoothing method to forecast the electric vehicles demand and its proportion to the whole automobile sales, and analyze the industrial development of Chinese electric vehicles by American EVs industry.
Vlasov, V. M.; Novikov, A. N.; Novikov, I. A.; Shevtsova, A. G.
2018-03-01
In the environment of highly developed urban agglomerations, one of the main problems arises - inability of the road network to reach a high level of motorization. The introduction of intelligent transport systems allows solving this problem, but the main issue in their implementation remains open: to what extent this or that method of improving the transport network will be effective and whether it is able to solve the problem of vehicle growth especially for the long-term period. The main goal of this work was the development of an approach to forecasting the increase in the intensity of traffic flow for a long-term period using the population and the level of motorization. The developed approach made it possible to determine the projected population and, taking into account the level of motorization, to determine the growth factor of the traffic flow intensity, which allows calculating the intensity value for a long-term period with high accuracy. The analysis of the main methods for predicting the characteristics of the transport stream is performed. The basic values and parameters necessary for their use are established. The analysis of the urban settlement is carried out and the level of motorization characteristic for the given locality is determined. A new approach to predicting the intensity of the traffic flow has been developed, which makes it possible to predict the change in the transport situation in the long term in high accuracy. Calculations of the magnitude of the intensity increase on the basis of the developed forecasting method are made and the errors in the data obtained are determined. The main recommendations on the use of the developed forecasting approach for the long-term functioning of the road network are formulated.
Methods of improvement of forecasting of development of mineral deposits' power supply
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Alexander V. Putilov
2015-03-01
Full Text Available Mineral deposits (among which non-ferrous metals take a leading place are situated on the territory of our planet rather unevenly, and often in out-of-the-way places. Nuclear power (particularly, transportable nuclear power plants provides the new possibilities of power supply, which is very important for deposits' development. This article shares the economic aspects of forecasting in the field of power development (in particular, nuclear power on the basis of transportable nuclear power plants. Economic barriers of development of innovative nuclear technologies are considered on the example of transportable nuclear power plants. At the same time, there are given the ways of elimination of such barrier to development of this technology as methodical absence of investigation of a question of distribution of added cost between producers of innovative equipment and final product. Addition of new analytical tool (“business diagonal” is offered for a method of definition of economically efficient distribution of added cost (received as a result of introduction of innovative technologies between participants of production and consumption of atomic energy within the “economic cross” model. There is offered the order of use of method of cash flows discounting at calculations between nuclear market participants. Economic methods, offered in this article, may be used in forecasting of development of other energy technologies and introduction of prospective energy equipment.
Treatment of Outliers via Interpolation Method with Neural Network Forecast Performances
Wahir, N. A.; Nor, M. E.; Rusiman, M. S.; Gopal, K.
2018-04-01
Outliers often lurk in many datasets, especially in real data. Such anomalous data can negatively affect statistical analyses, primarily normality, variance, and estimation aspects. Hence, handling the occurrences of outliers require special attention. Therefore, it is important to determine the suitable ways in treating outliers so as to ensure that the quality of the analyzed data is indeed high. As such, this paper discusses an alternative method to treat outliers via linear interpolation method. In fact, assuming outlier as a missing value in the dataset allows the application of the interpolation method to interpolate the outliers thus, enabling the comparison of data series using forecast accuracy before and after outlier treatment. With that, the monthly time series of Malaysian tourist arrivals from January 1998 until December 2015 had been used to interpolate the new series. The results indicated that the linear interpolation method, which was comprised of improved time series data, displayed better results, when compared to the original time series data in forecasting from both Box-Jenkins and neural network approaches.
Short-Term Wind Power Forecasting Based on Clustering Pre-Calculated CFD Method
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Yimei Wang
2018-04-01
Full Text Available To meet the increasing wind power forecasting (WPF demands of newly built wind farms without historical data, physical WPF methods are widely used. The computational fluid dynamics (CFD pre-calculated flow fields (CPFF-based WPF is a promising physical approach, which can balance well the competing demands of computational efficiency and accuracy. To enhance its adaptability for wind farms in complex terrain, a WPF method combining wind turbine clustering with CPFF is first proposed where the wind turbines in the wind farm are clustered and a forecasting is undertaken for each cluster. K-means, hierarchical agglomerative and spectral analysis methods are used to establish the wind turbine clustering models. The Silhouette Coefficient, Calinski-Harabaz index and within-between index are proposed as criteria to evaluate the effectiveness of the established clustering models. Based on different clustering methods and schemes, various clustering databases are built for clustering pre-calculated CFD (CPCC-based short-term WPF. For the wind farm case studied, clustering evaluation criteria show that hierarchical agglomerative clustering has reasonable results, spectral clustering is better and K-means gives the best performance. The WPF results produced by different clustering databases also prove the effectiveness of the three evaluation criteria in turn. The newly developed CPCC model has a much higher WPF accuracy than the CPFF model without using clustering techniques, both on temporal and spatial scales. The research provides supports for both the development and improvement of short-term physical WPF systems.
A forecasting method to reduce estimation bias in self-reported cell phone data.
Redmayne, Mary; Smith, Euan; Abramson, Michael J
2013-01-01
There is ongoing concern that extended exposure to cell phone electromagnetic radiation could be related to an increased risk of negative health effects. Epidemiological studies seek to assess this risk, usually relying on participants' recalled use, but recall is notoriously poor. Our objectives were primarily to produce a forecast method, for use by such studies, to reduce estimation bias in the recalled extent of cell phone use. The method we developed, using Bayes' rule, is modelled with data we collected in a cross-sectional cluster survey exploring cell phone user-habits among New Zealand adolescents. Participants recalled their recent extent of SMS-texting and retrieved from their provider the current month's actual use-to-date. Actual use was taken as the gold standard in the analyses. Estimation bias arose from a large random error, as observed in all cell phone validation studies. We demonstrate that this seriously exaggerates upper-end forecasts of use when used in regression models. This means that calculations using a regression model will lead to underestimation of heavy-users' relative risk. Our Bayesian method substantially reduces estimation bias. In cases where other studies' data conforms to our method's requirements, application should reduce estimation bias, leading to a more accurate relative risk calculation for mid-to-heavy users.
Distorted wave method in reactions with composite particles
International Nuclear Information System (INIS)
Zelenskaya, N.S.; Teplov, I.B.
1980-01-01
The work deals with the distorbed wave method with a finite radius of interaction (DWBAFR) as applied to quantitative analysis of direct nuclear reactions with composite particles (including heavy ions) considering the reaction mechanisms other than the cluster stripping mechanism, in particular the exchange processes. The accurate equations of the distorbed-wave method in the three-body problem and the general formula dor calculating differential cross-sections of arbitrary binary reactions by DWBAFR are presented. Accurate and approximate methods allowing for finite interaction radius are discussed. Two main versions of exact account of recoil effects: separation of variables in wave functions of relative motion of particles and in interaction potentials and separation of variables in distorted waves are analysed. Given is a characteristic of the known calculated programs approximately and exactly taking account of recoil effects for direct and exchange processes [ru
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A. Schepen
2018-03-01
Full Text Available Rainfall forecasts are an integral part of hydrological forecasting systems at sub-seasonal to seasonal timescales. In seasonal forecasting, global climate models (GCMs are now the go-to source for rainfall forecasts. For hydrological applications however, GCM forecasts are often biased and unreliable in uncertainty spread, and calibration is therefore required before use. There are sophisticated statistical techniques for calibrating monthly and seasonal aggregations of the forecasts. However, calibration of seasonal forecasts at the daily time step typically uses very simple statistical methods or climate analogue methods. These methods generally lack the sophistication to achieve unbiased, reliable and coherent forecasts of daily amounts and seasonal accumulated totals. In this study, we propose and evaluate a Rainfall Post-Processing method for Seasonal forecasts (RPP-S, which is based on the Bayesian joint probability modelling approach for calibrating daily forecasts and the Schaake Shuffle for connecting the daily ensemble members of different lead times. We apply the method to post-process ACCESS-S forecasts for 12 perennial and ephemeral catchments across Australia and for 12 initialisation dates. RPP-S significantly reduces bias in raw forecasts and improves both skill and reliability. RPP-S forecasts are also more skilful and reliable than forecasts derived from ACCESS-S forecasts that have been post-processed using quantile mapping, especially for monthly and seasonal accumulations. Several opportunities to improve the robustness and skill of RPP-S are identified. The new RPP-S post-processed forecasts will be used in ensemble sub-seasonal to seasonal streamflow applications.
Schepen, Andrew; Zhao, Tongtiegang; Wang, Quan J.; Robertson, David E.
2018-03-01
Rainfall forecasts are an integral part of hydrological forecasting systems at sub-seasonal to seasonal timescales. In seasonal forecasting, global climate models (GCMs) are now the go-to source for rainfall forecasts. For hydrological applications however, GCM forecasts are often biased and unreliable in uncertainty spread, and calibration is therefore required before use. There are sophisticated statistical techniques for calibrating monthly and seasonal aggregations of the forecasts. However, calibration of seasonal forecasts at the daily time step typically uses very simple statistical methods or climate analogue methods. These methods generally lack the sophistication to achieve unbiased, reliable and coherent forecasts of daily amounts and seasonal accumulated totals. In this study, we propose and evaluate a Rainfall Post-Processing method for Seasonal forecasts (RPP-S), which is based on the Bayesian joint probability modelling approach for calibrating daily forecasts and the Schaake Shuffle for connecting the daily ensemble members of different lead times. We apply the method to post-process ACCESS-S forecasts for 12 perennial and ephemeral catchments across Australia and for 12 initialisation dates. RPP-S significantly reduces bias in raw forecasts and improves both skill and reliability. RPP-S forecasts are also more skilful and reliable than forecasts derived from ACCESS-S forecasts that have been post-processed using quantile mapping, especially for monthly and seasonal accumulations. Several opportunities to improve the robustness and skill of RPP-S are identified. The new RPP-S post-processed forecasts will be used in ensemble sub-seasonal to seasonal streamflow applications.
Ding, Chuan; Wang, Kaihong; Huang, Xiaoying
2014-01-01
In a distribution channel, channel members are not always self-interested, but altruistic in some conditions. Based on this assumption, this paper adopts a behavior game method to analyze and forecast channel members’ decision behavior based on result fairness preference and reciprocal fairness preference by embedding a fair preference theory in channel research of coordination. The behavior game forecasts that a channel can achieve coordination if channel members consider behavior elemen...
Reflection and diffraction of atomic de Broglie waves by evanescent laser waves. Bare-state method
International Nuclear Information System (INIS)
Feng, Xiaoping; Witte, N.S.; Hollenberg, C.L.; Opat, G.
1994-01-01
Two methods are presented for the investigation of the reflection and diffraction of atoms by gratings formed either by standing or travelling evanescent laser waves. Both methods use the bare-state rather than dressed-state picture. One method is based on the Born series, whereas the other is based on the Laplace transformation of the coupled differential equations. The two methods yield the same theoretical expressions for the reflected and diffracted atomic waves in the whole space including the interaction and the asymptotic regions. 1 ref., 1 fig
Onken, Reiner
2017-11-01
A relocatable ocean prediction system (ROPS) was employed to an observational data set which was collected in June 2014 in the waters to the west of Sardinia (western Mediterranean) in the framework of the REP14-MED experiment. The observational data, comprising more than 6000 temperature and salinity profiles from a fleet of underwater gliders and shipborne probes, were assimilated in the Regional Ocean Modeling System (ROMS), which is the heart of ROPS, and verified against independent observations from ScanFish tows by means of the forecast skill score as defined by Murphy(1993). A simplified objective analysis (OA) method was utilised for assimilation, taking account of only those profiles which were located within a predetermined time window W. As a result of a sensitivity study, the highest skill score was obtained for a correlation length scale C = 12.5 km, W = 24 h, and r = 1, where r is the ratio between the error of the observations and the background error, both for temperature and salinity. Additional ROPS runs showed that (i) the skill score of assimilation runs was mostly higher than the score of a control run without assimilation, (i) the skill score increased with increasing forecast range, and (iii) the skill score for temperature was higher than the score for salinity in the majority of cases. Further on, it is demonstrated that the vast number of observations can be managed by the applied OA method without data reduction, enabling timely operational forecasts even on a commercially available personal computer or a laptop.
Energy Technology Data Exchange (ETDEWEB)
Cadenas, E. [Facultad de Ingenieria Mecanica, Universidad Michoacana de San Nicolas de Hidalgo, Santiago Tapia No. 403, Centro (Mexico); Jaramillo, O.A.; Rivera, W. [Centro de Ivestigacion en Energia, Universidad Nacional Autonoma de Mexico, Apartado Postal 34, Temixco 62580, Morelos (Mexico)
2010-05-15
In this paper the analysis and forecasting of wind velocities in Chetumal, Quintana Roo, Mexico is presented. Measurements were made by the Instituto de Investigaciones Electricas (IIE) during two years, from 2004 to 2005. This location exemplifies the wind energy generation potential in the Caribbean coast of Mexico that could be employed in the hotel industry in the next decade. The wind speed and wind direction were measured at 10 m above ground level. Sensors with high accuracy and a low starting threshold were used. The wind velocity was recorded using a data acquisition system supplied by a 10 W photovoltaic panel. The wind speed values were measured with a frequency of 1 Hz and the average wind speed was recorded considering regular intervals of 10 min. First a statistical analysis of the time series was made in the first part of the paper through conventional and robust measures. Also the forecasting of the last day of measurements was made utilizing the single exponential smoothing method (SES). The results showed a very good accuracy of the data with this technique for an {alpha} value of 0.9. Finally the SES method was compared with the artificial neural network (ANN) method showing the former better results. (author)
Multi-step polynomial regression method to model and forecast malaria incidence.
Directory of Open Access Journals (Sweden)
Chandrajit Chatterjee
Full Text Available Malaria is one of the most severe problems faced by the world even today. Understanding the causative factors such as age, sex, social factors, environmental variability etc. as well as underlying transmission dynamics of the disease is important for epidemiological research on malaria and its eradication. Thus, development of suitable modeling approach and methodology, based on the available data on the incidence of the disease and other related factors is of utmost importance. In this study, we developed a simple non-linear regression methodology in modeling and forecasting malaria incidence in Chennai city, India, and predicted future disease incidence with high confidence level. We considered three types of data to develop the regression methodology: a longer time series data of Slide Positivity Rates (SPR of malaria; a smaller time series data (deaths due to Plasmodium vivax of one year; and spatial data (zonal distribution of P. vivax deaths for the city along with the climatic factors, population and previous incidence of the disease. We performed variable selection by simple correlation study, identification of the initial relationship between variables through non-linear curve fitting and used multi-step methods for induction of variables in the non-linear regression analysis along with applied Gauss-Markov models, and ANOVA for testing the prediction, validity and constructing the confidence intervals. The results execute the applicability of our method for different types of data, the autoregressive nature of forecasting, and show high prediction power for both SPR and P. vivax deaths, where the one-lag SPR values plays an influential role and proves useful for better prediction. Different climatic factors are identified as playing crucial role on shaping the disease curve. Further, disease incidence at zonal level and the effect of causative factors on different zonal clusters indicate the pattern of malaria prevalence in the city
Spectral analysis of surface waves method to assess shear wave velocity within centrifuge models
MURILLO, Carol Andrea; THOREL, Luc; CAICEDO, Bernardo
2009-01-01
The method of the spectral analysis of surface waves (SASW) is tested out on reduced scale centrifuge models, with a specific device, called the mini Falling Weight, developed for this purpose. Tests are performed on layered materials made of a mixture of sand and clay. The shear wave velocity VS determined within the models using the SASW is compared with the laboratory measurements carried out using the bender element test. The results show that the SASW technique applied to centrifuge test...
DEFF Research Database (Denmark)
Duus, Henrik Johannsen
2016-01-01
Purpose: The purpose of this article is to present an overview of the area of strategic forecasting and its research directions and to put forward some ideas for improving management decisions. Design/methodology/approach: This article is conceptual but also informed by the author’s long contact...... and collaboration with various business firms. It starts by presenting an overview of the area and argues that the area is as much a way of thinking as a toolbox of theories and methodologies. It then spells out a number of research directions and ideas for management. Findings: Strategic forecasting is seen...... as a rebirth of long range planning, albeit with new methods and theories. Firms should make the building of strategic forecasting capability a priority. Research limitations/implications: The article subdivides strategic forecasting into three research avenues and suggests avenues for further research efforts...
A Novel 3D Viscoelastic Acoustic Wave Equation Based Update Method for Reservoir History Matching
Katterbauer, Klemens
2014-12-10
The oil and gas industry has been revolutionized within the last decade, with horizontal drilling and hydraulic fracturing enabling the extraction of huge amounts of shale gas in areas previously considered impossible and the recovering of hydrocarbons in harsh environments like the arctic or in previously unimaginable depths like the off-shore exploration in the South China sea and Gulf of Mexico. With the development of 4D seismic, engineers and scientists have been enabled to map the evolution of fluid fronts within the reservoir and determine the displacement caused by the injected fluids. This in turn has led to enhanced production strategies, cost reduction and increased profits. Conventional approaches to incorporate seismic data into the history matching process have been to invert these data for constraints that are subsequently employed in the history matching process. This approach makes the incorporation computationally expensive and requires a lot of manual processing for obtaining the correct interpretation due to the potential artifacts that are generated by the generally ill-conditioned inversion problems. I have presented here a novel approach via including the time-lapse cross-well seismic survey data directly into the history matching process. The generated time-lapse seismic data are obtained from the full wave 3D viscoelastic acoustic wave equation. Furthermore an extensive analysis has been performed showing the robustness of the method and enhanced forecastability of the critical reservoir parameters, reducing uncertainties and exhibiting the benefits of a full wave 3D seismic approach. Finally, the improved performance has been statistically confirmed. The improvements illustrate the significant improvements in forecasting that are obtained via readily available seismic data without the need for inversion. This further optimizes oil production in addition to increasing return-on-investment on oil & gas field development projects, especially
Directory of Open Access Journals (Sweden)
L. Schoon
2018-05-01
Full Text Available For the local diagnosis of wave properties, we develop, validate, and apply a novel method which is based on the Hilbert transform. It is called Unified Wave Diagnostics (UWaDi. It provides the wave amplitude and three-dimensional wave number at any grid point for gridded three-dimensional data. UWaDi is validated for a synthetic test case comprising two different wave packets. In comparison with other methods, the performance of UWaDi is very good with respect to wave properties and their location. For a first practical application of UWaDi, a minor sudden stratospheric warming on 30 January 2016 is chosen. Specifying the diagnostics for hydrostatic inertia–gravity waves in analyses from the European Centre for Medium-Range Weather Forecasts, we detect the local occurrence of gravity waves throughout the middle atmosphere. The local wave characteristics are discussed in terms of vertical propagation using the diagnosed local amplitudes and wave numbers. We also note some hints on local inertia–gravity wave generation by the stratospheric jet from the detection of shallow slow waves in the vicinity of its exit region.
Directory of Open Access Journals (Sweden)
Junfang Li
2016-01-01
Full Text Available Direct forecasting method for Urban Rail Transit (URT ridership at the station level is not able to reflect nonlinear relationship between ridership and its predictors. Also, population is inappropriately expressed in this method since it is not uniformly distributed by area. In this paper, a new variable, population per distance band, is considered and a back propagation neural network (BPNN model which can reflect nonlinear relationship between ridership and its predictors is proposed to forecast ridership. Key predictors are obtained through partial correlation analysis. The performance of the proposed model is compared with three other benchmark models, which are linear model with population per distance band, BPNN model with total population, and linear model with total population, using four measures of effectiveness (MOEs, maximum relative error (MRE, smallest relative error (SRE, average relative error (ARE, and mean square root of relative error (MSRRE. Also, another model for contribution rate of population per distance band to ridership is formulated based on the BPNN model with nonpopulation variables fixed. Case studies with Japanese data show that BPNN model with population per distance band outperforms other three models and the contribution rate of population within special distance band to ridership calculated through the contribution rate model is 70%~92.9% close to actual statistical value. The result confirms the effectiveness of models proposed in this paper.
Ionospheric forecasting model using fuzzy logic-based gradient descent method
Directory of Open Access Journals (Sweden)
D. Venkata Ratnam
2017-09-01
Full Text Available Space weather phenomena cause satellite to ground or satellite to aircraft transmission outages over the VHF to L-band frequency range, particularly in the low latitude region. Global Positioning System (GPS is primarily susceptible to this form of space weather. Faulty GPS signals are attributed to ionospheric error, which is a function of Total Electron Content (TEC. Importantly, precise forecasts of space weather conditions and appropriate hazard observant cautions required for ionospheric space weather observations are limited. In this paper, a fuzzy logic-based gradient descent method has been proposed to forecast the ionospheric TEC values. In this technique, membership functions have been tuned based on the gradient descent estimated values. The proposed algorithm has been tested with the TEC data of two geomagnetic storms in the low latitude station of KL University, Guntur, India (16.44°N, 80.62°E. It has been found that the gradient descent method performs well and the predicted TEC values are close to the original TEC measurements.
Galerkin finite element methods for wave problems
Indian Academy of Sciences (India)
basis functions (called G1FEM here) and quadratic basis functions (called G2FEM) ... mulation of Brookes & Hughes (1982) that implicitly incorporates numerical ..... functions and (c) SUPG method in the (kh − ω t)-plane for explicit Euler.
Input data preprocessing method for exchange rate forecasting via neural network
Directory of Open Access Journals (Sweden)
Antić Dragan S.
2014-01-01
Full Text Available The aim of this paper is to present a method for neural network input parameters selection and preprocessing. The purpose of this network is to forecast foreign exchange rates using artificial intelligence. Two data sets are formed for two different economic systems. Each system is represented by six categories with 70 economic parameters which are used in the analysis. Reduction of these parameters within each category was performed by using the principal component analysis method. Component interdependencies are established and relations between them are formed. Newly formed relations were used to create input vectors of a neural network. The multilayer feed forward neural network is formed and trained using batch training. Finally, simulation results are presented and it is concluded that input data preparation method is an effective way for preprocessing neural network data. [Projekat Ministarstva nauke Republike Srbije, br.TR 35005, br. III 43007 i br. III 44006
Finite element and discontinuous Galerkin methods for transient wave equations
Cohen, Gary
2017-01-01
This monograph presents numerical methods for solving transient wave equations (i.e. in time domain). More precisely, it provides an overview of continuous and discontinuous finite element methods for these equations, including their implementation in physical models, an extensive description of 2D and 3D elements with different shapes, such as prisms or pyramids, an analysis of the accuracy of the methods and the study of the Maxwell’s system and the important problem of its spurious free approximations. After recalling the classical models, i.e. acoustics, linear elastodynamics and electromagnetism and their variational formulations, the authors present a wide variety of finite elements of different shapes useful for the numerical resolution of wave equations. Then, they focus on the construction of efficient continuous and discontinuous Galerkin methods and study their accuracy by plane wave techniques and a priori error estimates. A chapter is devoted to the Maxwell’s system and the important problem ...
Comparison of matrix methods for elastic wave scattering problems
International Nuclear Information System (INIS)
Tsao, S.J.; Varadan, V.K.; Varadan, V.V.
1983-01-01
This article briefly describes the T-matrix method and the MOOT (method of optimal truncation) of elastic wave scattering as they apply to A-D, SH- wave problems as well as 3-D elastic wave problems. Two methods are compared for scattering by elliptical cylinders as well as oblate spheroids of various eccentricity as a function of frequency. Convergence, and symmetry of the scattering cross section are also compared for ellipses and spheroidal cavities of different aspect ratios. Both the T-matrix approach and the MOOT were programmed on an AMDHL 470 computer using double precision arithmetic. Although the T-matrix method and MOOT are not always in agreement, it is in no way implied that any of the published results using MOOT are in error
Wave field restoration using three-dimensional Fourier filtering method.
Kawasaki, T; Takai, Y; Ikuta, T; Shimizu, R
2001-11-01
A wave field restoration method in transmission electron microscopy (TEM) was mathematically derived based on a three-dimensional (3D) image formation theory. Wave field restoration using this method together with spherical aberration correction was experimentally confirmed in through-focus images of amorphous tungsten thin film, and the resolution of the reconstructed phase image was successfully improved from the Scherzer resolution limit to the information limit. In an application of this method to a crystalline sample, the surface structure of Au(110) was observed in a profile-imaging mode. The processed phase image showed quantitatively the atomic relaxation of the topmost layer.
A Spatiotemporal Multi-View-Based Learning Method for Short-Term Traffic Forecasting
Directory of Open Access Journals (Sweden)
Shifen Cheng
2018-06-01
Full Text Available Short-term traffic forecasting plays an important part in intelligent transportation systems. Spatiotemporal k-nearest neighbor models (ST-KNNs have been widely adopted for short-term traffic forecasting in which spatiotemporal matrices are constructed to describe traffic conditions. The performance of the models is closely related to the spatial dependencies, the temporal dependencies, and the interaction of spatiotemporal dependencies. However, these models use distance functions and correlation coefficients to identify spatial neighbors and measure the temporal interaction by only considering the temporal closeness of traffic, which result in existing ST-KNNs that cannot fully reflect the essential features of road traffic. This study proposes an improved spatiotemporal k-nearest neighbor model for short-term traffic forecasting by utilizing a multi-view learning algorithm named MVL-STKNN that fully considers the spatiotemporal dependencies of traffic data. First, the spatial neighbors for each road segment are automatically determined using cross-correlation under different temporal dependencies. Three spatiotemporal views are built on the constructed spatiotemporal closeness, periodic, and trend matrices to represent spatially heterogeneous traffic states. Second, a spatiotemporal weighting matrix is introduced into the ST-KNN model to recognize similar traffic patterns in the three spatiotemporal views. Finally, the results of traffic pattern recognition under these three spatiotemporal views are aggregated by using a neural network algorithm to describe the interaction of spatiotemporal dependencies. Extensive experiments were conducted using real vehicular-speed datasets collected on city roads and expressways. In comparison with baseline methods, the results show that the MVL-STKNN model greatly improves short-term traffic forecasting by lowering the mean absolute percentage error between 28.24% and 46.86% for the city road dataset and
Chiral metamaterials characterisation using the wave propagation retrieval method
DEFF Research Database (Denmark)
Andryieuski, Andrei; Lavrinenko, Andrei; Malureanu, Radu
2010-01-01
In this presentation we extend the wave propagation method for the retrieval of the effective properties to the case of chiral metamaterials with circularly polarised eigenwaves. The method is unambiguous, simple and provides bulk effective parameters. Advantages and constraints are discussed...
Directory of Open Access Journals (Sweden)
Yolcu Ufuk
2016-06-01
Full Text Available Fuzzy time series methods based on the fuzzy set theory proposed by Zadeh (1965 was first introduced by Song and Chissom (1993. Since fuzzy time series methods do not have the assumptions that traditional time series do and have effective forecasting performance, the interest on fuzzy time series approaches is increasing rapidly. Fuzzy time series methods have been used in almost all areas, such as environmental science, economy and finance. The concepts of labour force participation and unemployment have great importance in terms of both the economy and sociology of countries. For this reason there are many studies on their forecasting. In this study, we aim to forecast the labour force participation and unemployment rate in Poland and Turkey using different fuzzy time series methods.
Liu, Li; Gao, Chao; Xuan, Weidong; Xu, Yue-Ping
2017-11-01
Ensemble flood forecasts by hydrological models using numerical weather prediction products as forcing data are becoming more commonly used in operational flood forecasting applications. In this study, a hydrological ensemble flood forecasting system comprised of an automatically calibrated Variable Infiltration Capacity model and quantitative precipitation forecasts from TIGGE dataset is constructed for Lanjiang Basin, Southeast China. The impacts of calibration strategies and ensemble methods on the performance of the system are then evaluated. The hydrological model is optimized by the parallel programmed ε-NSGA II multi-objective algorithm. According to the solutions by ε-NSGA II, two differently parameterized models are determined to simulate daily flows and peak flows at each of the three hydrological stations. Then a simple yet effective modular approach is proposed to combine these daily and peak flows at the same station into one composite series. Five ensemble methods and various evaluation metrics are adopted. The results show that ε-NSGA II can provide an objective determination on parameter estimation, and the parallel program permits a more efficient simulation. It is also demonstrated that the forecasts from ECMWF have more favorable skill scores than other Ensemble Prediction Systems. The multimodel ensembles have advantages over all the single model ensembles and the multimodel methods weighted on members and skill scores outperform other methods. Furthermore, the overall performance at three stations can be satisfactory up to ten days, however the hydrological errors can degrade the skill score by approximately 2 days, and the influence persists until a lead time of 10 days with a weakening trend. With respect to peak flows selected by the Peaks Over Threshold approach, the ensemble means from single models or multimodels are generally underestimated, indicating that the ensemble mean can bring overall improvement in forecasting of flows. For
The verification of operational surface wave forecast system in the South China Sea%南海海浪业务化数值预报系统检验
Institute of Scientific and Technical Information of China (English)
周水华; 俞胜宾; 梁昌霞; 冯伟忠; 吴迪生
2012-01-01
In order to estimate the Operational Surface Wave Forecast System of South China Sea Marine Fore-cast Center, SOA, by using the observational data during March-November in 2010 and 2011 in the South Chi-na Sea, the 24 h ,48 h, 72 h forecast result from the System is verified. The statistical results show that the predic-tion error of significant wave height and mean period is 24 h<48 h< 72h, and the average absolute error of signif-icant wave height forecasted in 24h, 48h, 72h is less than 0.5m. The average absolute error in the average period of 24h, 48h , 72 h is less than 0.8s. Meanwhile, the forecast error is significantly smaller in October and November than that in other months. Regression analysis revealed that the forecast value and the observed value exists in highly linear correlation relationship, and with the prediction time growth, the correlation is gradually decreasing, and overall the forecast values are larger than observed value. In conclusion, the system which forecast error is ac-ceptable and meets the basic requirements of operational forecasting. However, there are still larger gaps between this system and other similar system, such as ECMWF .%为检验南海海浪业务化数值预报系统的预报效果,利用2010年和2011年3-11月的观测资料,通过计算预报值和观测值的绝对误差、相对误差等统计参数和线性回归分析对南海海浪业务化数值预报系统进行检验.统计结果显示有效波高和平均周期的预报误差24 h＜48 h＜72 h,有效波高的24 h、48 h、72 h预报平均绝对误差小于0.5 m,平均周期的24 h、48 h、72 h预报平均绝对误差小于0.8 s；预报误差有明显的季节变化,10月和11月的预报误差显著小于其它各月；回归分析结果显示预报值与观测值存在中度高度线性相关关系,随着预报时效的增长相关度逐渐递减,预报值较观测值偏大.总体来说,该系统的预报误差在可接受的范围之内,满足业务化
Wang, Wen-Chuan; Chau, Kwok-Wing; Cheng, Chun-Tian; Qiu, Lin
2009-08-01
SummaryDeveloping a hydrological forecasting model based on past records is crucial to effective hydropower reservoir management and scheduling. Traditionally, time series analysis and modeling is used for building mathematical models to generate hydrologic records in hydrology and water resources. Artificial intelligence (AI), as a branch of computer science, is capable of analyzing long-series and large-scale hydrological data. In recent years, it is one of front issues to apply AI technology to the hydrological forecasting modeling. In this paper, autoregressive moving-average (ARMA) models, artificial neural networks (ANNs) approaches, adaptive neural-based fuzzy inference system (ANFIS) techniques, genetic programming (GP) models and support vector machine (SVM) method are examined using the long-term observations of monthly river flow discharges. The four quantitative standard statistical performance evaluation measures, the coefficient of correlation ( R), Nash-Sutcliffe efficiency coefficient ( E), root mean squared error (RMSE), mean absolute percentage error (MAPE), are employed to evaluate the performances of various models developed. Two case study river sites are also provided to illustrate their respective performances. The results indicate that the best performance can be obtained by ANFIS, GP and SVM, in terms of different evaluation criteria during the training and validation phases.
A Bayesian method to rank different model forecasts of the same volcanic ash cloud: Chapter 24
Denlinger, Roger P.; Webley, P.; Mastin, Larry G.; Schwaiger, Hans F.
2012-01-01
Volcanic eruptions often spew fine ash high into the atmosphere, where it is carried downwind, forming long ash clouds that disrupt air traffic and pose a hazard to air travel. To mitigate such hazards, the community studying ash hazards must assess risk of ash ingestion for any flight path and provide robust and accurate forecasts of volcanic ash dispersal. We provide a quantitative and objective method to evaluate the efficacy of ash dispersal estimates from different models, using Bayes theorem to assess the predictions that each model makes about ash dispersal. We incorporate model and measurement uncertainty and produce a posterior probability for model input parameters. The integral of the posterior over all possible combinations of model inputs determines the evidence for each model and is used to compare models. We compare two different types of transport models, an Eulerian model (Ash3d) and a Langrangian model (PUFF), as applied to the 2010 eruptions of Eyjafjallajökull volcano in Iceland. The evidence for each model benefits from common physical characteristics of ash dispersal from an eruption column and provides a measure of how well each model forecasts cloud transport. Given the complexity of the wind fields, we find that the differences between these models depend upon the differences in the way the models disperse ash into the wind from the source plume. With continued observation, the accuracy of the estimates made by each model increases, increasing the efficacy of each model’s ability to simulate ash dispersal.
A method to forecast quantitative variables relating to nuclear public acceptance
International Nuclear Information System (INIS)
Ohnishi, T.
1992-01-01
A methodology is proposed for forecasting the future trend of quantitative variables profoundly related to the public acceptance (PA) of nuclear energy. The social environment influencing PA is first modeled by breaking it down into a finite number of fundamental elements and then the interactive formulae between the quantitative variables, which are attributed to and characterize each element, are determined by using the actual values of the variables in the past. Inputting the estimated values of exogenous variables into these formulae, the forecast values of endogenous variables can finally be obtained. Using this method, the problem of nuclear PA in Japan is treated as, for example, where the context is considered to comprise a public sector and the general social environment and socio-psychology. The public sector is broken down into three elements of the general public, the inhabitants living around nuclear facilities and the activists of anti-nuclear movements, whereas the social environment and socio-psychological factors are broken down into several elements, such as news media and psychological factors. Twenty-seven endogenous and seven exogenous variables are introduced to quantify these elements. After quantitatively formulating the interactive features between them and extrapolating the exogenous variables into the future estimates are made of the growth or attenuation of the endogenous variables, such as the pro- and anti-nuclear fractions in public opinion polls and the frequency of occurrence of anti-nuclear movements. (author)
El-Jaat, Majda; Hulley, Michael; Tétreault, Michel
2018-02-01
Despite the broad impact and importance of saltwater intrusion in coastal aquifers, little research has been directed towards forecasting saltwater intrusion in areas where the source of saltwater is uncertain. Saline contamination in inland groundwater supplies is a concern for numerous communities in the southern US including the city of Deltona, Florida. Furthermore, conventional numerical tools for forecasting saltwater contamination are heavily dependent on reliable characterization of the physical characteristics of underlying aquifers, information that is often absent or challenging to obtain. To overcome these limitations, a reliable alternative data-driven model for forecasting salinity in a groundwater supply was developed for Deltona using the fast orthogonal search (FOS) method. FOS was applied on monthly water-demand data and corresponding chloride concentrations at water supply wells. Groundwater salinity measurements from Deltona water supply wells were applied to evaluate the forecasting capability and accuracy of the FOS model. Accurate and reliable groundwater salinity forecasting is necessary to support effective and sustainable coastal-water resource planning and management. The available (27) water supply wells for Deltona were randomly split into three test groups for the purposes of FOS model development and performance assessment. Based on four performance indices (RMSE, RSR, NSEC, and R), the FOS model proved to be a reliable and robust forecaster of groundwater salinity. FOS is relatively inexpensive to apply, is not based on rigorous physical characterization of the water supply aquifer, and yields reliable estimates of groundwater salinity in active water supply wells.
Spectral analysis of surface waves method to assess shear wave velocity within centrifuge models
Murillo, Carol Andrea; Thorel, Luc; Caicedo, Bernardo
2009-06-01
The method of the spectral analysis of surface waves (SASW) is tested out on reduced scale centrifuge models, with a specific device, called the mini Falling Weight, developed for this purpose. Tests are performed on layered materials made of a mixture of sand and clay. The shear wave velocity VS determined within the models using the SASW is compared with the laboratory measurements carried out using the bender element test. The results show that the SASW technique applied to centrifuge testing is a relevant method to characterize VS near the surface.
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.
An Unconditionally Stable Method for Solving the Acoustic Wave Equation
Directory of Open Access Journals (Sweden)
Zhi-Kai Fu
2015-01-01
Full Text Available An unconditionally stable method for solving the time-domain acoustic wave equation using Associated Hermit orthogonal functions is proposed. The second-order time derivatives in acoustic wave equation are expanded by these orthogonal basis functions. By applying Galerkin temporal testing procedure, the time variable can be eliminated from the calculations. The restriction of Courant-Friedrichs-Levy (CFL condition in selecting time step for analyzing thin layer can be avoided. Numerical results show the accuracy and the efficiency of the proposed method.
Wang, Jun; Zhou, Bi-hua; Zhou, Shu-dao; Sheng, Zheng
2015-01-01
The paper proposes a novel function expression method to forecast chaotic time series, using an improved genetic-simulated annealing (IGSA) algorithm to establish the optimum function expression that describes the behavior of time series. In order to deal with the weakness associated with the genetic algorithm, the proposed algorithm incorporates the simulated annealing operation which has the strong local search ability into the genetic algorithm to enhance the performance of optimization; besides, the fitness function and genetic operators are also improved. Finally, the method is applied to the chaotic time series of Quadratic and Rossler maps for validation. The effect of noise in the chaotic time series is also studied numerically. The numerical results verify that the method can forecast chaotic time series with high precision and effectiveness, and the forecasting precision with certain noise is also satisfactory. It can be concluded that the IGSA algorithm is energy-efficient and superior.
Singh, Navneet K.; Singh, Asheesh K.; Tripathy, Manoj
2012-05-01
For power industries electricity load forecast plays an important role for real-time control, security, optimal unit commitment, economic scheduling, maintenance, energy management, and plant structure planning etc. A new technique for long term load forecasting (LTLF) using optimized feed forward artificial neural network (FFNN) architecture is presented in this paper, which selects optimal number of neurons in the hidden layer as well as the best training method for the case study. The prediction performance of proposed technique is evaluated using mean absolute percentage error (MAPE) of Thailand private electricity consumption and forecasted data. The results obtained are compared with the results of classical auto-regressive (AR) and moving average (MA) methods. It is, in general, observed that the proposed method is prediction wise more accurate.
Damage detection in composite materials using Lamb wave methods
Kessler, Seth S.; Spearing, S. Mark; Soutis, Constantinos
2002-04-01
Cost-effective and reliable damage detection is critical for the utilization of composite materials. This paper presents part of an experimental and analytical survey of candidate methods for in situ damage detection of composite materials. Experimental results are presented for the application of Lamb wave techniques to quasi-isotropic graphite/epoxy test specimens containing representative damage modes, including delamination, transverse ply cracks and through-holes. Linear wave scans were performed on narrow laminated specimens and sandwich beams with various cores by monitoring the transmitted waves with piezoceramic sensors. Optimal actuator and sensor configurations were devised through experimentation, and various types of driving signal were explored. These experiments provided a procedure capable of easily and accurately determining the time of flight of a Lamb wave pulse between an actuator and sensor. Lamb wave techniques provide more information about damage presence and severity than previously tested methods (frequency response techniques), and provide the possibility of determining damage location due to their local response nature. These methods may prove suitable for structural health monitoring applications since they travel long distances and can be applied with conformable piezoelectric actuators and sensors that require little power.
A robust absorbing layer method for anisotropic seismic wave modeling
Energy Technology Data Exchange (ETDEWEB)
Métivier, L., E-mail: ludovic.metivier@ujf-grenoble.fr [LJK, CNRS, Université de Grenoble, BP 53, 38041 Grenoble Cedex 09 (France); ISTerre, Université de Grenoble I, BP 53, 38041 Grenoble Cedex 09 (France); Brossier, R. [ISTerre, Université de Grenoble I, BP 53, 38041 Grenoble Cedex 09 (France); Labbé, S. [LJK, CNRS, Université de Grenoble, BP 53, 38041 Grenoble Cedex 09 (France); Operto, S. [Géoazur, Université de Nice Sophia-Antipolis, CNRS, IRD, OCA, Villefranche-sur-Mer (France); Virieux, J. [ISTerre, Université de Grenoble I, BP 53, 38041 Grenoble Cedex 09 (France)
2014-12-15
When applied to wave propagation modeling in anisotropic media, Perfectly Matched Layers (PML) exhibit instabilities. Incoming waves are amplified instead of being absorbed. Overcoming this difficulty is crucial as in many seismic imaging applications, accounting accurately for the subsurface anisotropy is mandatory. In this study, we present the SMART layer method as an alternative to PML approach. This method is based on the decomposition of the wavefield into components propagating inward and outward the domain of interest. Only outgoing components are damped. We show that for elastic and acoustic wave propagation in Transverse Isotropic media, the SMART layer is unconditionally dissipative: no amplification of the wavefield is possible. The SMART layers are not perfectly matched, therefore less accurate than conventional PML. However, a reasonable increase of the layer size yields an accuracy similar to PML. Finally, we illustrate that the selective damping strategy on which is based the SMART method can prevent the generation of spurious S-waves by embedding the source in a small zone where only S-waves are damped.
A robust absorbing layer method for anisotropic seismic wave modeling
International Nuclear Information System (INIS)
Métivier, L.; Brossier, R.; Labbé, S.; Operto, S.; Virieux, J.
2014-01-01
When applied to wave propagation modeling in anisotropic media, Perfectly Matched Layers (PML) exhibit instabilities. Incoming waves are amplified instead of being absorbed. Overcoming this difficulty is crucial as in many seismic imaging applications, accounting accurately for the subsurface anisotropy is mandatory. In this study, we present the SMART layer method as an alternative to PML approach. This method is based on the decomposition of the wavefield into components propagating inward and outward the domain of interest. Only outgoing components are damped. We show that for elastic and acoustic wave propagation in Transverse Isotropic media, the SMART layer is unconditionally dissipative: no amplification of the wavefield is possible. The SMART layers are not perfectly matched, therefore less accurate than conventional PML. However, a reasonable increase of the layer size yields an accuracy similar to PML. Finally, we illustrate that the selective damping strategy on which is based the SMART method can prevent the generation of spurious S-waves by embedding the source in a small zone where only S-waves are damped
Environmental data processing by clustering methods for energy forecast and planning
Energy Technology Data Exchange (ETDEWEB)
Di Piazza, Annalisa [Dipartimento di Ingegneria Idraulica e Applicazioni Ambientali (DIIAA), viale delle Scienze, Universita degli Studi di Palermo, 90128 Palermo (Italy); Di Piazza, Maria Carmela; Ragusa, Antonella; Vitale, Gianpaolo [Consiglio Nazionale delle Ricerche Istituto di Studi sui Sistemi Intelligenti per l' Automazione (ISSIA - CNR), sezione di Palermo, Via Dante, 12, 90141 Palermo (Italy)
2011-03-15
This paper presents a statistical approach based on the k-means clustering technique to manage environmental sampled data to evaluate and to forecast of the energy deliverable by different renewable sources in a given site. In particular, wind speed and solar irradiance sampled data are studied in association to the energy capability of a wind generator and a photovoltaic (PV) plant, respectively. The proposed method allows the sub-sets of useful data, describing the energy capability of a site, to be extracted from a set of experimental observations belonging the considered site. The data collection is performed in Sicily, in the south of Italy, as case study. As far as the wind generation is concerned, a suitable generator, matching the wind profile of the studied sites, has been selected for the evaluation of the producible energy. With respect to the photovoltaic generation, the irradiance data have been taken from the acquisition system of an actual installation. It is demonstrated, in both cases, that the use of the k-means clustering method allows data that do not contribute to the produced energy to be grouped into a cluster, moreover it simplifies the problem of the energy assessment since it permits to obtain the desired information on energy capability by managing a reduced amount of experimental samples. In the studied cases, the proposed method permitted a reduction of the 50% of the data with a maximum discrepancy of 10% in energy estimation compared to the classical statistical approach. Therefore, the adopted k-means clustering technique represents an useful tool for an appropriate and less demanding energy forecast and planning in distributed generation systems. (author)
Are demand forecasting techniques applicable to libraries?
Sridhar, M. S.
1984-01-01
Examines the nature and limitations of demand forecasting, discuses plausible methods of forecasting demand for information, suggests some useful hints for demand forecasting and concludes by emphasizing unified approach to demand forecasting.
A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method
Jun-He Yang; Ching-Hsue Cheng; Chia-Pan Chan
2017-01-01
Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir's water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting m...
Simulation of the acoustic wave propagation using a meshless method
Directory of Open Access Journals (Sweden)
Bajko J.
2017-01-01
Full Text Available This paper presents numerical simulations of the acoustic wave propagation phenomenon modelled via Linearized Euler equations. A meshless method based on collocation of the strong form of the equation system is adopted. Moreover, the Weighted least squares method is used for local approximation of derivatives as well as stabilization technique in a form of spatial ltering. The accuracy and robustness of the method is examined on several benchmark problems.
Numerical simulation methods for wave propagation through optical waveguides
International Nuclear Information System (INIS)
Sharma, A.
1993-01-01
The simulation of the field propagation through waveguides requires numerical solutions of the Helmholtz equation. For this purpose a method based on the principle of orthogonal collocation was recently developed. The method is also applicable to nonlinear pulse propagation through optical fibers. Some of the salient features of this method and its application to both linear and nonlinear wave propagation through optical waveguides are discussed in this report. 51 refs, 8 figs, 2 tabs
Energy Technology Data Exchange (ETDEWEB)
Sanderson, D.; O' Hare, M.
1977-05-01
Models forecasting second-order impacts from energy development vary in their methodology, output, assumptions, and quality. As a rough dichotomy, they either simulate community development over time or combine various submodels providing community snapshots at selected points in time. Using one or more methods - input/output models, gravity models, econometric models, cohort-survival models, or coefficient models - they estimate energy-development-stimulated employment, population, public and private service needs, and government revenues and expenditures at some future time (ranging from annual to average year predictions) and for different governmental jurisdictions (municipal, county, state, etc.). Underlying assumptions often conflict, reflecting their different sources - historical data, comparative data, surveys, and judgments about future conditions. Model quality, measured by special features, tests, exportability and usefulness to policy-makers, reveals careful and thorough work in some cases and hurried operations with insufficient in-depth analysis in others.
Application of data mining methods for power forecast of wind power plants
Energy Technology Data Exchange (ETDEWEB)
Arnoldt, Alexander; Koenig, Stefan; Bretschneider, Peter [Fraunhofer Institute for Optronics, System Technology, and Image Exploitation - Application Centre System Technology (IOSB-AST), Ilmenau (Germany). Energy Systems Group; Mikut, Ralf [Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen (DE). Inst. for Applied Computer Science (IAI)
2010-07-01
Since the last decade power systems underlie a drastic change due to increased exploitation of renewable energy resources (RES) such as wind and photovoltaic power plants. A result of this process is a significant increase of fluctuating generation in low, middle and high voltage grids. Consequently, impacts on short and middle term capacity planning of power plants occur and must be handled to avoid imbalances between generation and demand at any time. Therefore, forecasts of wind and photovoltaic generation play a very important role. Quality improvements potentially ease planning and lead to cost reductions. This work investigated the dependencies of input parameters. The optimal parameter selection was achieved through application of data mining methods. Finally, the wind power prediction was demonstrated with Artificial Neural Networks and Physical Models. (orig.)
Gan, Ruijing; Chen, Xiaojun; Yan, Yu; Huang, Daizheng
2015-01-01
Accurate incidence forecasting of infectious disease provides potentially valuable insights in its own right. It is critical for early prevention and may contribute to health services management and syndrome surveillance. This study investigates the use of a hybrid algorithm combining grey model (GM) and back propagation artificial neural networks (BP-ANN) to forecast hepatitis B in China based on the yearly numbers of hepatitis B and to evaluate the method's feasibility. The results showed that the proposal method has advantages over GM (1, 1) and GM (2, 1) in all the evaluation indexes.
Day ahead forecast of wind power through optimal application of multivariate analyzing methods
Energy Technology Data Exchange (ETDEWEB)
Arnoldt, Alexander; Bretschneider, Peter [Fraunhofer Institute for Optronics, System Technology, and Image Exploitation - Application Centre System Technology (IOSB-AST), Ilmenau (Germany). Energy Systems Group
2011-07-01
This paper presents two algorithms in identifying input models for artificial neural networks. The algorithms are based on an entropy analysis and an eigenvalue analysis of the correlation matrix. The resulting input models are used for investigating a feed forward and a recurrent artificial neural network structure to simulate a 24 hour forecast of wind power production. The limitation of the forecast error distribution is investigated through successful implementation of hybridization of single forecast models. Errors of the best forecast model stay between a normalized root mean square error from 3.5% to 6.1%. (orig.)
Evaluation of Statistical Methods for Modeling Historical Resource Production and Forecasting
Nanzad, Bolorchimeg
This master's thesis project consists of two parts. Part I of the project compares modeling of historical resource production and forecasting of future production trends using the logit/probit transform advocated by Rutledge (2011) with conventional Hubbert curve fitting, using global coal production as a case study. The conventional Hubbert/Gaussian method fits a curve to historical production data whereas a logit/probit transform uses a linear fit to a subset of transformed production data. Within the errors and limitations inherent in this type of statistical modeling, these methods provide comparable results. That is, despite that apparent goodness-of-fit achievable using the Logit/Probit methodology, neither approach provides a significant advantage over the other in either explaining the observed data or in making future projections. For mature production regions, those that have already substantially passed peak production, results obtained by either method are closely comparable and reasonable, and estimates of ultimately recoverable resources obtained by either method are consistent with geologically estimated reserves. In contrast, for immature regions, estimates of ultimately recoverable resources generated by either of these alternative methods are unstable and thus, need to be used with caution. Although the logit/probit transform generates high quality-of-fit correspondence with historical production data, this approach provides no new information compared to conventional Gaussian or Hubbert-type models and may have the effect of masking the noise and/or instability in the data and the derived fits. In particular, production forecasts for immature or marginally mature production systems based on either method need to be regarded with considerable caution. Part II of the project investigates the utility of a novel alternative method for multicyclic Hubbert modeling tentatively termed "cycle-jumping" wherein overlap of multiple cycles is limited. The
Directory of Open Access Journals (Sweden)
Gabriella Ferruzzi
2013-02-01
Full Text Available A new short-term probabilistic forecasting method is proposed to predict the probability density function of the hourly active power generated by a photovoltaic system. Firstly, the probability density function of the hourly clearness index is forecasted making use of a Bayesian auto regressive time series model; the model takes into account the dependence of the solar radiation on some meteorological variables, such as the cloud cover and humidity. Then, a Monte Carlo simulation procedure is used to evaluate the predictive probability density function of the hourly active power by applying the photovoltaic system model to the random sampling of the clearness index distribution. A numerical application demonstrates the effectiveness and advantages of the proposed forecasting method.
Directory of Open Access Journals (Sweden)
Chun-tian Cheng
2015-07-01
Full Text Available Accurate daily runoff forecasting is of great significance for the operation control of hydropower station and power grid. Conventional methods including rainfall-runoff models and statistical techniques usually rely on a number of assumptions, leading to some deviation from the exact results. Artificial neural network (ANN has the advantages of high fault-tolerance, strong nonlinear mapping and learning ability, which provides an effective method for the daily runoff forecasting. However, its training has certain drawbacks such as time-consuming, slow learning speed and easily falling into local optimum, which cannot be ignored in the real world application. In order to overcome the disadvantages of ANN model, the artificial neural network model based on quantum-behaved particle swarm optimization (QPSO, ANN-QPSO for short, is presented for the daily runoff forecasting in this paper, where QPSO was employed to select the synaptic weights and thresholds of ANN, while ANN was used for the prediction. The proposed model can combine the advantages of both QPSO and ANN to enhance the generalization performance of the forecasting model. The methodology is assessed by using the daily runoff data of Hongjiadu reservoir in southeast Guizhou province of China from 2006 to 2014. The results demonstrate that the proposed approach achieves much better forecast accuracy than the basic ANN model, and the QPSO algorithm is an alternative training technique for the ANN parameters selection.
The extended (G/G)-expansion method and travelling wave ...
Indian Academy of Sciences (India)
Home; Journals; Pramana – Journal of Physics; Volume 82; Issue 6. The extended (′/)-expansion method and travelling wave solutions for the perturbed nonlinear Schrödinger's equation with Kerr law nonlinearity. Zaiyun Zhang Jianhua Huang Juan Zhong Sha-Sha Dou Jiao Liu Dan Peng Ting Gao. Research Articles ...
Quadratic algebras in the noncommutative integration method of wave equation
International Nuclear Information System (INIS)
Varaksin, O.L.
1995-01-01
The paper deals with the investigation of applications of the method of noncommutative integration of linear differential equations by partial derivatives. Nontrivial example was taken for integration of three-dimensions wave equation with the use of non-Abelian quadratic algebras
Analysis of Different Methods for Wave Generation and Absorption in a CFD-Based Numerical Wave Tank
Directory of Open Access Journals (Sweden)
Adria Moreno Miquel
2018-06-01
Full Text Available In this paper, the performance of different wave generation and absorption methods in computational fluid dynamics (CFD-based numerical wave tanks (NWTs is analyzed. The open-source CFD code REEF3D is used, which solves the Reynolds-averaged Navier–Stokes (RANS equations to simulate two-phase flow problems. The water surface is computed with the level set method (LSM, and turbulence is modeled with the k-ω model. The NWT includes different methods to generate and absorb waves: the relaxation method, the Dirichlet-type method and active wave absorption. A sensitivity analysis has been conducted in order to quantify and compare the differences in terms of absorption quality between these methods. A reflection analysis based on an arbitrary number of wave gauges has been adopted to conduct the study. Tests include reflection analysis of linear, second- and fifth-order Stokes waves, solitary waves, cnoidal waves and irregular waves generated in an NWT. Wave breaking over a sloping bed and wave forces on a vertical cylinder are calculated, and the influence of the reflections on the wave breaking location and the wave forces on the cylinder is investigated. In addition, a comparison with another open-source CFD code, OpenFOAM, has been carried out based on published results. Some differences in the calculated quantities depending on the wave generation and absorption method have been observed. The active wave absorption method is seen to be more efficient for long waves, whereas the relaxation method performs better for shorter waves. The relaxation method-based numerical beach generally results in lower reflected waves in the wave tank for most of the cases simulated in this study. The comparably better performance of the relaxation method comes at the cost of larger computational requirements due to the relaxation zones that have to be included in the domain. The reflections in the NWT in REEF3D are generally lower than the published results for
Linear density response function in the projector augmented wave method
DEFF Research Database (Denmark)
Yan, Jun; Mortensen, Jens Jørgen; Jacobsen, Karsten Wedel
2011-01-01
We present an implementation of the linear density response function within the projector-augmented wave method with applications to the linear optical and dielectric properties of both solids, surfaces, and interfaces. The response function is represented in plane waves while the single...... functions of Si, C, SiC, AlP, and GaAs compare well with previous calculations. While optical properties of semiconductors, in particular excitonic effects, are generally not well described by ALDA, we obtain excellent agreement with experiments for the surface loss function of graphene and the Mg(0001...
Comparison of Transmission Line Methods for Surface Acoustic Wave Modeling
Wilson, William; Atkinson, Gary
2009-01-01
Surface Acoustic Wave (SAW) technology is low cost, rugged, lightweight, extremely low power and can be used to develop passive wireless sensors. For these reasons, NASA is investigating the use of SAW technology for Integrated Vehicle Health Monitoring (IVHM) of aerospace structures. To facilitate rapid prototyping of passive SAW sensors for aerospace applications, SAW models have been developed. This paper reports on the comparison of three methods of modeling SAWs. The three models are the Impulse Response Method (a first order model), and two second order matrix methods; the conventional matrix approach, and a modified matrix approach that is extended to include internal finger reflections. The second order models are based upon matrices that were originally developed for analyzing microwave circuits using transmission line theory. Results from the models are presented with measured data from devices. Keywords: Surface Acoustic Wave, SAW, transmission line models, Impulse Response Method.
A Simplified Short Term Load Forecasting Method Based on Sequential Patterns
DEFF Research Database (Denmark)
Kouzelis, Konstantinos; Bak-Jensen, Birgitte; Mahat, Pukar
2014-01-01
Load forecasting is an essential part of a power system both for planning and daily operation purposes. As far as the latter is concerned, short term load forecasting has been broadly used at the transmission level. However, recent technological advancements and legislation have facilitated the i...... in comparison with an ARIMA model....
Software selection based on analysis and forecasting methods, practised in 1C
Vazhdaev, A. N.; Chernysheva, T. Y.; Lisacheva, E. I.
2015-09-01
The research focuses on the problem of a “1C: Enterprise 8” platform inboard mechanisms for data analysis and forecasting. It is important to evaluate and select proper software to develop effective strategies for customer relationship management in terms of sales, as well as implementation and further maintenance of software. Research data allows creating new forecast models to schedule further software distribution.
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.
Probabilistic methods for seasonal forecasting in a changing climate: Cox-type regression models
Maia, A.H.N.; Meinke, H.B.
2010-01-01
For climate risk management, cumulative distribution functions (CDFs) are an important source of information. They are ideally suited to compare probabilistic forecasts of primary (e.g. rainfall) or secondary data (e.g. crop yields). Summarised as CDFs, such forecasts allow an easy quantitative
Allergenic weed pollen forecast under the mathematical modeling method implementation in ukraine.
Motruk, Irina I; Antomonov, Michael Yu; Rodinkova, Victoria V; Aleksandrova, Olena E; Yermishev, Oleh V
2018-01-01
Introduction: Allergies are the most common reason of the chronic diseases in developed countries and represent an important medical, social and economic issue, the relevance of which is growing both in these countries and in Ukraine. The most famous of these allergens group is the pollen of ambrosia and pollen of poaceae, which are ubiquitously distributed in the subtropical and temperate climate. The aim: The objective of our study was to develop the mathematical models, which will be able to indicate the probability of the pollen circulation, and thus these models can simplify the forecast of symptoms risk and improve the prophylaxis of pollinosis. Materials and methods: The research was conducted on the basis of the research center of National Pirogov Memorial Medical University, Vinnytsia in the years 2012-2014. A volumetric sampler of the Hirst type was used for the air sampling. The observation was conducted from the first of April to the thirty-first of October. For the initial preparation of the tables and intermediate calculations, Excel software package was used. The software STATISTICA 10.0 was applied to calculate the average coefficients values and their statistical characteristics (beta-values, errors of the mean values, Student's t-test, veracity and the factors percentage contribution into the function variation). Results: Statistically significant correlation between pollen concentrations of herbaceous plants and individual meteorological factors was found; classificational functions were designed by which it is possible to calculate the probability of presence or absence of Artemisia pollen in the atmosphere; the risks of increasing of the Artemisia pollen concentration are determined under exceeding of the critical temperature of 18°С, relative humidity of 67% and atmospheric pressure of 980 Pa. Conclusions. The results of the research can be used to predict the emission of potentially hazardous concentrations of weed pollen grains in the
Spatial Dynamics Methods for Solitary Waves on a Ferrofluid Jet
Groves, M. D.; Nilsson, D. V.
2018-04-01
This paper presents existence theories for several families of axisymmetric solitary waves on the surface of an otherwise cylindrical ferrofluid jet surrounding a stationary metal rod. The ferrofluid, which is governed by a general (nonlinear) magnetisation law, is subject to an azimuthal magnetic field generated by an electric current flowing along the rod. The ferrohydrodynamic problem for axisymmetric travelling waves is formulated as an infinite-dimensional Hamiltonian system in which the axial direction is the time-like variable. A centre-manifold reduction technique is employed to reduce the system to a locally equivalent Hamiltonian system with a finite number of degrees of freedom, and homoclinic solutions to the reduced system, which correspond to solitary waves, are detected by dynamical-systems methods.
Directory of Open Access Journals (Sweden)
Shaofeng Xie
2017-01-01
Full Text Available Given the chaotic characteristics of the time series of landslides, a new method based on modified ensemble empirical mode decomposition (MEEMD, approximate entropy and the weighted least square support vector machine (WLS-SVM was proposed. The method mainly started from the chaotic sequence of time-frequency analysis and improved the model performance as follows: first a deformation time series was decomposed into a series of subsequences with significantly different complexity using MEEMD. Then the approximate entropy method was used to generate a new subsequence for the combination of subsequences with similar complexity, which could effectively concentrate the component feature information and reduce the computational scale. Finally the WLS-SVM prediction model was established for each new subsequence. At the same time, phase space reconstruction theory and the grid search method were used to select the input dimension and the optimal parameters of the model, and then the superposition of each predicted value was the final forecasting result. Taking the landslide deformation data of Danba as an example, the experiments were carried out and compared with wavelet neural network, support vector machine, least square support vector machine and various combination schemes. The experimental results show that the algorithm has high prediction accuracy. It can ensure a better prediction effect even in landslide deformation periods of rapid fluctuation, and it can also better control the residual value and effectively reduce the error interval.
A Study on the Determination of the World Crude Oil Price and Methods for Its Forecast
Energy Technology Data Exchange (ETDEWEB)
Kim, J.K. [Korea Energy Economics Institute, Euiwang (Korea)
2001-11-01
The primary purpose of this report is to provide the groundwork to develop the methods to forecast the world crude oil price. The methodology is used by both literature survey and empirical study. For this purpose, first of all, this report reviewed the present situation and the outlook of the world oil market based on oil demand, supply and prices. This analysis attempted to provide a deeper understanding to support the development of oil forecasting methods. The result of this review, in general, showed that the oil demand will be maintained annually at an average rate of around 2.4% under assumption that oil supply has no problem until 2020. The review showed that crude oil price will be a 3% increasing rate annually in the 1999 real term. This report used the contents of the summary review as reference data in order to link the KEEIOF model. In an effort to further investigate the contents of oil political economy, this report reviewed the articles of political economy about oil industry. It pointed out that the world oil industry is experiencing the change of restructuring oil industry after the Gulf War in 1990. The contents of restructuring oil industry are characterized by the 'open access' to resources not only in the Persian Gulf, but elsewhere in the world as well - especially the Caspian Sea Basin. In addition, the contents showed that the oil industries are shifted from government control to government and industry cooperation after the Gulf War. In order to examine the characters and the problems surrounding oil producing countries, this report described the model of OPEC behavior and strategy of oil management with political and military factors. Among examining the models of OPEC behavior, this report focused on hybrid model to explain OPEC behavior. In reviewing political and religious power structure in the Middle East, the report revealed that US emphasizes the importance of the Middle East for guaranteeing oil security. However, three
Directory of Open Access Journals (Sweden)
Ruijing Gan
2015-01-01
Full Text Available Accurate incidence forecasting of infectious disease provides potentially valuable insights in its own right. It is critical for early prevention and may contribute to health services management and syndrome surveillance. This study investigates the use of a hybrid algorithm combining grey model (GM and back propagation artificial neural networks (BP-ANN to forecast hepatitis B in China based on the yearly numbers of hepatitis B and to evaluate the method’s feasibility. The results showed that the proposal method has advantages over GM (1, 1 and GM (2, 1 in all the evaluation indexes.
Space Weather Forecasting at IZMIRAN
Gaidash, S. P.; Belov, A. V.; Abunina, M. A.; Abunin, A. A.
2017-12-01
Since 1998, the Institute of Terrestrial Magnetism, Ionosphere, and Radio Wave Propagation (IZMIRAN) has had an operating heliogeophysical service—the Center for Space Weather Forecasts. This center transfers the results of basic research in solar-terrestrial physics into daily forecasting of various space weather parameters for various lead times. The forecasts are promptly available to interested consumers. This article describes the center and the main types of forecasts it provides: solar and geomagnetic activity, magnetospheric electron fluxes, and probabilities of proton increases. The challenges associated with the forecasting of effects of coronal mass ejections and coronal holes are discussed. Verification data are provided for the center's forecasts.
Energy Technology Data Exchange (ETDEWEB)
Minato, Y. [Shikoku Research Institute Inc., Kagawa (Japan); Yokoi, Y. [The University of Tokushima, Tokushima (Japan)
1996-01-20
This paper relates to the forecasting method of the electric power demands (kWh and kW) of a region, approached by not only time series analysis but economic and social indexes. Those indexes, based on historical statistics such as census and establishment statistics, are rearranged from an administrative division to a managerial division of the electric power company, and applied as fundamental information for forecasting the area`s kWh and also sales promotion. This method of forecasting the area`s kWh is based on the concept that area`s kWh is strongly connected with the population their lifestyle and their activity within the region. In the paper, the framework of the computational model system and forecast result are discussed. The population, number of households and their members, and number of employed persons, are all evaluated. The forecasting method of the area`s population proposed here is based on the concept that the transition of population consists of both natural growth and immigration. By estimating both factors, the future area`s population can be easily forecasted. The information of whether the population is increasing or decreasing is useful for forecasting the region`s kWh and required sales promotion. 8 refs., 8 figs., 3 tabs.
Directory of Open Access Journals (Sweden)
Bohanec Marko
2017-08-01
Full Text Available Background and Purpose: The process of business to business (B2B sales forecasting is a complex decision-making process. There are many approaches to support this process, but mainly it is still based on the subjective judgment of a decision-maker. The problem of B2B sales forecasting can be modeled as a classification problem. However, top performing machine learning (ML models are black boxes and do not support transparent reasoning. The purpose of this research is to develop an organizational model using ML model coupled with general explanation methods. The goal is to support the decision-maker in the process of B2B sales forecasting.
Robust Approaches to Forecasting
Jennifer Castle; David Hendry; Michael P. Clements
2014-01-01
We investigate alternative robust approaches to forecasting, using a new class of robust devices, contrasted with equilibrium correction models. Their forecasting properties are derived facing a range of likely empirical problems at the forecast origin, including measurement errors, implulses, omitted variables, unanticipated location shifts and incorrectly included variables that experience a shift. We derive the resulting forecast biases and error variances, and indicate when the methods ar...
Application of Classification Methods for Forecasting Mid-Term Power Load Patterns
Piao, Minghao; Lee, Heon Gyu; Park, Jin Hyoung; Ryu, Keun Ho
Currently an automated methodology based on data mining techniques is presented for the prediction of customer load patterns in long duration load profiles. The proposed approach in this paper consists of three stages: (i) data preprocessing: noise or outlier is removed and the continuous attribute-valued features are transformed to discrete values, (ii) cluster analysis: k-means clustering is used to create load pattern classes and the representative load profiles for each class and (iii) classification: we evaluated several supervised learning methods in order to select a suitable prediction method. According to the proposed methodology, power load measured from AMR (automatic meter reading) system, as well as customer indexes, were used as inputs for clustering. The output of clustering was the classification of representative load profiles (or classes). In order to evaluate the result of forecasting load patterns, the several classification methods were applied on a set of high voltage customers of the Korea power system and derived class labels from clustering and other features are used as input to produce classifiers. Lastly, the result of our experiments was presented.
A tuning method for nonuniform traveling-wave accelerating structures
International Nuclear Information System (INIS)
Gong Cunkui; Zheng Shuxin; Shao Jiahang; Jia Xiaoyu; Chen Huaibi
2013-01-01
The tuning method of uniform traveling-wave structures based on non-resonant perturbation field distribution measurement has been widely used in tuning both constant-impedance and constant-gradient structures. In this paper, the method of tuning nonuniform structures is proposed on the basis of the above theory. The internal reflection coefficient of each cell is obtained from analyzing the normalized voltage distribution. A numerical simulation of tuning process according to the coupled cavity chain theory has been done and the result shows each cell is in right phase advance after tuning. The method will be used in the tuning of a disk-loaded traveling-wave structure being developed at the Accelerator Laboratory, Tsinghua University. (authors)
Microstrip natural wave spectrum mathematical model using partial inversion method
International Nuclear Information System (INIS)
Pogarsky, S.A.; Litvinenko, L.N.; Prosvirnin, S.L.
1995-01-01
It is generally agreed that both microstrip lines itself and different discontinuities based on microstrips are the most difficult problem for accurate electrodynamic analysis. Over the last years much has been published about principles and accurate (or full wave) methods of microstrip lines investigations. The growing interest for this problem may be explained by the microstrip application in the millimeter-wave range for purpose of realizing interconnects and a variety of passive components. At these higher operating rating frequencies accurate component modeling becomes more critical. A creation, examination and experimental verification of the accurate method for planar electrodynamical structures natural wave spectrum investigations are the objects of this manuscript. The moment method with partial inversion operator method using may be considered as a basical way for solving this problem. This method is outlook for accurate analysis of different planar discontinuities in microstrip: such as step discontinuities, microstrip turns, Y- and X-junctions and etc., substrate space steps dielectric constants and other anisotropy types
S-wave velocity measurements along levees in New Orleans using passive surface wave methods
Hayashi, K.; Lorenzo, J. M.; Craig, M. S.; Gostic, A.
2017-12-01
In order to develop non-invasive methods for levee inspection, geophysical investigations were carried out at four sites along levees in the New Orleans area: 17th Street Canal, London Avenue Canal, Marrero Levee, and Industrial Canal. Three of the four sites sustained damage from Hurricane Katrina in 2005 and have since been rebuilt. The geophysical methods used include active and passive surface wave methods, and capacitively coupled resistivity. This paper summarizes the acquisition and analysis of the 1D and 2D passive surface wave data. Twelve wireless seismic data acquisition units with 2 Hz vertical component geophones were used to record data. Each unit includes a GPS receiver so that all units can be synchronized over any distance without cables. The 1D passive method used L shaped arrays of three different sizes with geophone spacing ranging from 5 to 340 m. Ten minutes to one hour of ambient noise was recorded with each array, and total data acquisition took approximately two hours at each site. The 2D method used a linear array with a geophone spacing of 5m. Four geophones were moved forward every 10 minutes along 400 1000 m length lines. Data acquisition took several hours for each line. Recorded ambient noise was processed using the spatial autocorrelation method and clear dispersion curves were obtained at all sites (Figure 1a). Minimum frequencies ranged from 0.4 to 0.7 Hz and maximum frequencies ranged from 10 to 30 Hz depending on the site. Non-linear inversion was performed and 1D and 2D S-wave velocity models were obtained. The 1D method penetrated to depths ranging from 200 to 500 m depending on the site (Figure 1b). The 2D method penetrated to a depth of 40 60 m and provided 400 1000 m cross sections along the levees (Figure 2). The interpretation focused on identifying zones beneath the levees or canal walls having low S-wave velocities corresponding to saturated, unconsolidated sands, or low-rigidity clays. Resultant S-wave velocity profiles
International Nuclear Information System (INIS)
Saparov, A.
1977-01-01
Changes of volume weight, volume numidity, side friction and head resistance of loess rocks are considered. It is established, that the most perspective methods for forecasting engineering-geological properties of loess rocks are the methods of radioactivity logging and static probing. The quantitative determinations of physical and mechanical properties are made using the data of the following geophysical methods: gamma-gamma logging, neutron logging and gamma logging
Full wave simulation of waves in ECRIS plasmas based on the finite element method
Energy Technology Data Exchange (ETDEWEB)
Torrisi, G. [INFN - Laboratori Nazionali del Sud, via S. Sofia 62, 95123, Catania, Italy and Università Mediterranea di Reggio Calabria, Dipartimento di Ingegneria dell' Informazione, delle Infrastrutture e dell' Energia Sostenibile (DIIES), Via Graziella, I (Italy); Mascali, D.; Neri, L.; Castro, G.; Patti, G.; Celona, L.; Gammino, S.; Ciavola, G. [INFN - Laboratori Nazionali del Sud, via S. Sofia 62, 95123, Catania (Italy); Di Donato, L. [Università degli Studi di Catania, Dipartimento di Ingegneria Elettrica Elettronica ed Informatica (DIEEI), Viale Andrea Doria 6, 95125 Catania (Italy); Sorbello, G. [INFN - Laboratori Nazionali del Sud, via S. Sofia 62, 95123, Catania, Italy and Università degli Studi di Catania, Dipartimento di Ingegneria Elettrica Elettronica ed Informatica (DIEEI), Viale Andrea Doria 6, 95125 Catania (Italy); Isernia, T. [Università Mediterranea di Reggio Calabria, Dipartimento di Ingegneria dell' Informazione, delle Infrastrutture e dell' Energia Sostenibile (DIIES), Via Graziella, I-89100 Reggio Calabria (Italy)
2014-02-12
This paper describes the modeling and the full wave numerical simulation of electromagnetic waves propagation and absorption in an anisotropic magnetized plasma filling the resonant cavity of an electron cyclotron resonance ion source (ECRIS). The model assumes inhomogeneous, dispersive and tensorial constitutive relations. Maxwell's equations are solved by the finite element method (FEM), using the COMSOL Multiphysics{sup ®} suite. All the relevant details have been considered in the model, including the non uniform external magnetostatic field used for plasma confinement, the local electron density profile resulting in the full-3D non uniform magnetized plasma complex dielectric tensor. The more accurate plasma simulations clearly show the importance of cavity effect on wave propagation and the effects of a resonant surface. These studies are the pillars for an improved ECRIS plasma modeling, that is mandatory to optimize the ion source output (beam intensity distribution and charge state, especially). Any new project concerning the advanced ECRIS design will take benefit by an adequate modeling of self-consistent wave absorption simulations.
Annual electricity consumption analysis and forecasting of China based on few observations methods
International Nuclear Information System (INIS)
Meng Ming; Niu Dongxiao
2011-01-01
The annual electricity consumption analysis and forecasting of China is one of the important bases of management decision making for power generation groups as well as power policy adjusting for government. The socioeconomic actuality could not offer adequate observations with perfect statistic characters. The partial least squares method is applied to get a linear equation. It could quantificational simulate the relationship between the electricity consumption and its factors. The variables importance analysis method is further adopted to distinguish the explanatory power of all relative factors. The foremost importance of production and consumption in rural area shows that the development of this area should account more for the increasing of electricity consumption. The less explanatory power of the gross domestic product of tertiary industry means the gigantic potential in electricity consumption for the future several years. At last, it calculates the contributions of observations. The results show that the unusual development of real estate and relative industry has affected the usual electricity consumption mode. With the clear away of price bubble in real estate, the increasing speed of electricity consumption will slow down in the recent years.
Annual electricity consumption analysis and forecasting of China based on few observations methods
Energy Technology Data Exchange (ETDEWEB)
Meng, Ming; Niu, Dongxiao [School of Business Administration, North China Electric Power University, 071003 Baoding (China)
2011-02-15
The annual electricity consumption analysis and forecasting of China is one of the important bases of management decision making for power generation groups as well as power policy adjusting for government. The socioeconomic actuality could not offer adequate observations with perfect statistic characters. The partial least squares method is applied to get a linear equation. It could quantificational simulate the relationship between the electricity consumption and its factors. The variables importance analysis method is further adopted to distinguish the explanatory power of all relative factors. The foremost importance of production and consumption in rural area shows that the development of this area should account more for the increasing of electricity consumption. The less explanatory power of the gross domestic product of tertiary industry means the gigantic potential in electricity consumption for the future several years. At last, it calculates the contributions of observations. The results show that the unusual development of real estate and relative industry has affected the usual electricity consumption mode. With the clear away of price bubble in real estate, the increasing speed of electricity consumption will slow down in the recent years. (author)
Annual electricity consumption analysis and forecasting of China based on few observations methods
Energy Technology Data Exchange (ETDEWEB)
Meng Ming, E-mail: ncepumm@126.co [School of Business Administration, North China Electric Power University, 071003 Baoding (China); Niu Dongxiao [School of Business Administration, North China Electric Power University, 071003 Baoding (China)
2011-02-15
The annual electricity consumption analysis and forecasting of China is one of the important bases of management decision making for power generation groups as well as power policy adjusting for government. The socioeconomic actuality could not offer adequate observations with perfect statistic characters. The partial least squares method is applied to get a linear equation. It could quantificational simulate the relationship between the electricity consumption and its factors. The variables importance analysis method is further adopted to distinguish the explanatory power of all relative factors. The foremost importance of production and consumption in rural area shows that the development of this area should account more for the increasing of electricity consumption. The less explanatory power of the gross domestic product of tertiary industry means the gigantic potential in electricity consumption for the future several years. At last, it calculates the contributions of observations. The results show that the unusual development of real estate and relative industry has affected the usual electricity consumption mode. With the clear away of price bubble in real estate, the increasing speed of electricity consumption will slow down in the recent years.
Electricity demand forecasting techniques
International Nuclear Information System (INIS)
Gnanalingam, K.
1994-01-01
Electricity demand forecasting plays an important role in power generation. The two areas of data that have to be forecasted in a power system are peak demand which determines the capacity (MW) of the plant required and annual energy demand (GWH). Methods used in electricity demand forecasting include time trend analysis and econometric methods. In forecasting, identification of manpower demand, identification of key planning factors, decision on planning horizon, differentiation between prediction and projection (i.e. development of different scenarios) and choosing from different forecasting techniques are important
Integral Equation Methods for Electromagnetic and Elastic Waves
Chew, Weng; Hu, Bin
2008-01-01
Integral Equation Methods for Electromagnetic and Elastic Waves is an outgrowth of several years of work. There have been no recent books on integral equation methods. There are books written on integral equations, but either they have been around for a while, or they were written by mathematicians. Much of the knowledge in integral equation methods still resides in journal papers. With this book, important relevant knowledge for integral equations are consolidated in one place and researchers need only read the pertinent chapters in this book to gain important knowledge needed for integral eq
Directory of Open Access Journals (Sweden)
Nantian Huang
2016-09-01
Full Text Available The prediction accuracy of short-term load forecast (STLF depends on prediction model choice and feature selection result. In this paper, a novel random forest (RF-based feature selection method for STLF is proposed. First, 243 related features were extracted from historical load data and the time information of prediction points to form the original feature set. Subsequently, the original feature set was used to train an RF as the original model. After the training process, the prediction error of the original model on the test set was recorded and the permutation importance (PI value of each feature was obtained. Then, an improved sequential backward search method was used to select the optimal forecasting feature subset based on the PI value of each feature. Finally, the optimal forecasting feature subset was used to train a new RF model as the final prediction model. Experiments showed that the prediction accuracy of RF trained by the optimal forecasting feature subset was higher than that of the original model and comparative models based on support vector regression and artificial neural network.
Directory of Open Access Journals (Sweden)
Levi Lopes Teixeira
2015-12-01
Full Text Available Time series forecasting is widely used in various areas of human knowledge, especially in the planning and strategic direction of companies. The success of this task depends on the forecasting techniques applied. In this paper, a hybrid approach to project time series is suggested. To validate the methodology, a time series already modeled by other authors was chosen, allowing the comparison of results. The proposed methodology includes the following techniques: wavelet shrinkage, wavelet decomposition at level r, and artificial neural networks (ANN. Firstly, a time series to be forecasted is submitted to the proposed wavelet filtering method, which decomposes it to components of trend and linear residue. Then, both are decomposed via level r wavelet decomposition, generating r + 1 Wavelet Components (WCs for each one; and then each WC is individually modeled by an ANN. Finally, the predictions for all WCs are linearly combined, producing forecasts to the underlying time series. For evaluating purposes, the time series of Canadian Lynx has been used, and all results achieved by the proposed method were better than others in existing literature.
Ike, P.; Voogd, Henk; Voogd, Henk; Linden, Gerard
2004-01-01
This chapter begins with a discussion of qualitative forecasting by describing a number of methods that depend on judgements made by stakeholders, experts or other interested parties to arrive at forecasts. Two qualitative approaches are illuminated, the Delphi and scenario methods respectively. Quantitative forecasting is illustrated with a brief overview of time series methods. Both qualitative and quantitative methods are illustrated by an example. The role and relative importance of forec...
DEFF Research Database (Denmark)
Wied Pedersen, Jonas; Lund, Nadia Schou Vorndran; Borup, Morten
2016-01-01
High quality on-line flow forecasts are useful for real-time operation of urban drainage systems and wastewater treatment plants. This requires computationally efficient models, which are continuously updated with observed data to provide good initial conditions for the forecasts. This paper...... period of time that precedes the forecast. The method is illustrated for an urban catchment, where flow forecasts of 0–4 h are generated by applying a lumped linear reservoir model with three cascading reservoirs. Radar rainfall observations are used as input to the model. The effects of different prior...
Masato, Giacomo; Cavany, Sean; Charlton-Perez, Andrew; Dacre, Helen; Bone, Angie; Carmicheal, Katie; Murray, Virginia; Danker, Rutger; Neal, Rob; Sarran, Christophe
2015-04-01
The health forecasting alert system for cold weather and heatwaves currently in use in the Cold Weather and Heatwave plans for England is based on 5 alert levels, with levels 2 and 3 dependent on a forecast or actual single temperature action trigger. Epidemiological evidence indicates that for both heat and cold, the impact on human health is gradual, with worsening impact for more extreme temperatures. The 60% risk of heat and cold forecasts used by the alerts is a rather crude probabilistic measure, which could be substantially improved thanks to the state-of-the-art forecast techniques. In this study a prototype of a new health forecasting alert system is developed, which is aligned to the approach used in the Met Office's (MO) National Severe Weather Warning Service (NSWWS). This is in order to improve information available to responders in the health and social care system by linking temperatures more directly to risks of mortality, and developing a system more coherent with other weather alerts. The prototype is compared to the current system in the Cold Weather and Heatwave plans via a case-study approach to verify its potential advantages and shortcomings. The prototype health forecasting alert system introduces an "impact vs likelihood matrix" for the health impacts of hot and cold temperatures which is similar to those used operationally for other weather hazards as part of the NSWWS. The impact axis of this matrix is based on existing epidemiological evidence, which shows an increasing relative risk of death at extremes of outdoor temperature beyond a threshold which can be identified epidemiologically. The likelihood axis is based on a probability measure associated with the temperature forecast. The new method is tested for two case studies (one during summer 2013, one during winter 2013), and compared to the performance of the current alert system. The prototype shows some clear improvements over the current alert system. It allows for a much greater
International Nuclear Information System (INIS)
Shang Yadong
2008-01-01
The extended hyperbolic functions method for nonlinear wave equations is presented. Based on this method, we obtain a multiple exact explicit solutions for the nonlinear evolution equations which describe the resonance interaction between the long wave and the short wave. The solutions obtained in this paper include (a) the solitary wave solutions of bell-type for S and L, (b) the solitary wave solutions of kink-type for S and bell-type for L, (c) the solitary wave solutions of a compound of the bell-type and the kink-type for S and L, (d) the singular travelling wave solutions, (e) periodic travelling wave solutions of triangle function types, and solitary wave solutions of rational function types. The variety of structure to the exact solutions of the long-short wave equation is illustrated. The methods presented here can also be used to obtain exact solutions of nonlinear wave equations in n dimensions
Methods for use in detecting seismic waves in a borehole
West, Phillip B.; Fincke, James R.; Reed, Teddy R.
2007-02-20
The invention provides methods and apparatus for detecting seismic waves propagating through a subterranean formation surrounding a borehole. In a first embodiment, a sensor module uses the rotation of bogey wheels to extend and retract a sensor package for selective contact and magnetic coupling to casing lining the borehole. In a second embodiment, a sensor module is magnetically coupled to the casing wall during its travel and dragged therealong while maintaining contact therewith. In a third embodiment, a sensor module is interfaced with the borehole environment to detect seismic waves using coupling through liquid in the borehole. Two or more of the above embodiments may be combined within a single sensor array to provide a resulting seismic survey combining the optimum of the outputs of each embodiment into a single data set.
Directory of Open Access Journals (Sweden)
Konstantinos Salpasaranis
2012-01-01
Full Text Available The introduction of a hybrid genetic programming method (hGP in fitting and forecasting of the broadband penetration data is proposed. The hGP uses some well-known diffusion models, such as those of Gompertz, Logistic, and Bass, in the initial population of the solutions in order to accelerate the algorithm. The produced solutions models of the hGP are used in fitting and forecasting the adoption of broadband penetration. We investigate the fitting performance of the hGP, and we use the hGP to forecast the broadband penetration in OECD (Organisation for Economic Co-operation and Development countries. The results of the optimized diffusion models are compared to those of the hGP-generated models. The comparison indicates that the hGP manages to generate solutions with high-performance statistical indicators. The hGP cooperates with the existing diffusion models, thus allowing multiple approaches to forecasting. The modified algorithm is implemented in the Python programming language, which is fast in execution time, compact, and user friendly.
Energy Technology Data Exchange (ETDEWEB)
Shao, Aimei; Xu, Daosheng [Lanzhou Univ. (China). Key Lab. of Arid Climatic Changing and Reducing Disaster of Gansu Province; Chinese Academy of Meteorological Sciences, Beijing (China). State Key Lab. of Severe Weather; Qiu, Xiaobin [Lanzhou Univ. (China). Key Lab. of Arid Climatic Changing and Reducing Disaster of Gansu Province; Tianjin Institute of Meteorological Science (China); Qiu, Chongjian [Lanzhou Univ. (China). Key Lab. of Arid Climatic Changing and Reducing Disaster of Gansu Province
2013-02-15
In the ensemble-based four dimensional variational assimilation method (SVD-En4DVar), a singular value decomposition (SVD) technique is used to select the leading eigenvectors and the analysis variables are expressed as the orthogonal bases expansion of the eigenvectors. The experiments with a two-dimensional shallow-water equation model and simulated observations show that the truncation error and rejection of observed signals due to the reduced-dimensional reconstruction of the analysis variable are the major factors that damage the analysis when the ensemble size is not large enough. However, a larger-sized ensemble is daunting computational burden. Experiments with a shallow-water equation model also show that the forecast error covariances remain relatively constant over time. For that reason, we propose an approach that increases the members of the forecast ensemble while reducing the update frequency of the forecast error covariance in order to increase analysis accuracy and to reduce the computational cost. A series of experiments were conducted with the shallow-water equation model to test the efficiency of this approach. The experimental results indicate that this approach is promising. Further experiments with the WRF model show that this approach is also suitable for the real atmospheric data assimilation problem, but the update frequency of the forecast error covariances should not be too low. (orig.)
Modelling viscoacoustic wave propagation with the lattice Boltzmann method.
Xia, Muming; Wang, Shucheng; Zhou, Hui; Shan, Xiaowen; Chen, Hanming; Li, Qingqing; Zhang, Qingchen
2017-08-31
In this paper, the lattice Boltzmann method (LBM) is employed to simulate wave propagation in viscous media. LBM is a kind of microscopic method for modelling waves through tracking the evolution states of a large number of discrete particles. By choosing different relaxation times in LBM experiments and using spectrum ratio method, we can reveal the relationship between the quality factor Q and the parameter τ in LBM. A two-dimensional (2D) homogeneous model and a two-layered model are tested in the numerical experiments, and the LBM results are compared against the reference solution of the viscoacoustic equations based on the Kelvin-Voigt model calculated by finite difference method (FDM). The wavefields and amplitude spectra obtained by LBM coincide with those by FDM, which demonstrates the capability of the LBM with one relaxation time. The new scheme is relatively simple and efficient to implement compared with the traditional lattice methods. In addition, through a mass of experiments, we find that the relaxation time of LBM has a quantitative relationship with Q. Such a novel scheme offers an alternative forward modelling kernel for seismic inversion and a new model to describe the underground media.
International Nuclear Information System (INIS)
Voyant, Cyril; Motte, Fabrice; Fouilloy, Alexis; Notton, Gilles; Paoli, Christophe; Nivet, Marie-Laure
2017-01-01
Integration of unpredictable renewable energy sources into electrical networks intensifies the complexity of the grid management due to their intermittent and unforeseeable nature. Because of the strong increase of solar power generation the prediction of solar yields becomes more and more important. Electrical operators need an estimation of the future production. For nowcasting and short term forecasting, the usual technics based on machine learning need large historical data sets of good quality during the training phase of predictors. However data are not always available and induce an advanced maintenance of meteorological stations, making the method inapplicable for poor instrumented or isolated sites. In this work, we propose intuitive methodologies based on the Kalman filter use (also known as linear quadratic estimation), able to predict a global radiation time series without the need of historical data. The accuracy of these methods is compared to other classical data driven methods, for different horizons of prediction and time steps. The proposed approach shows interesting capabilities allowing to improve quasi-systematically the prediction. For one to 10 h horizons Kalman model performances are competitive in comparison to more sophisticated models such as ANN which require both consistent historical data sets and computational resources. - Highlights: • Solar radiation forecasting with time series formalism. • Trainless approach compared to machine learning methods. • Very simple method dedicated to solar irradiation forecasting with high accuracy.
Zakiyatussariroh, W. H. Wan; Said, Z. Mohammad; Norazan, M. R.
2014-12-01
This study investigated the performance of the Lee-Carter (LC) method and it variants in modeling and forecasting Malaysia mortality. These include the original LC, the Lee-Miller (LM) variant and the Booth-Maindonald-Smith (BMS) variant. These methods were evaluated using Malaysia's mortality data which was measured based on age specific death rates (ASDR) for 1971 to 2009 for overall population while those for 1980-2009 were used in separate models for male and female population. The performance of the variants has been examined in term of the goodness of fit of the models and forecasting accuracy. Comparison was made based on several criteria namely, mean square error (MSE), root mean square error (RMSE), mean absolute deviation (MAD) and mean absolute percentage error (MAPE). The results indicate that BMS method was outperformed in in-sample fitting for overall population and when the models were fitted separately for male and female population. However, in the case of out-sample forecast accuracy, BMS method only best when the data were fitted to overall population. When the data were fitted separately for male and female, LCnone performed better for male population and LM method is good for female population.
International Nuclear Information System (INIS)
Masera, D; Bocca, P; Grazzini, A
2011-01-01
In this experimental program the main goal is to monitor the damage evolution in masonry and concrete structures by Acoustic Emission (AE) signal analysis applying a well-know seismic method. For this reason the concept of the coda wave interferometry is applied to AE signal recorded during the tests. Acoustic Emission (AE) are very effective non-destructive techniques applied to identify micro and macro-defects and their temporal evolution in several materials. This technique permits to estimate the velocity of ultrasound waves propagation and the amount of energy released during fracture propagation to obtain information on the criticality of the ongoing process. By means of AE monitoring, an experimental analysis on a set of reinforced masonry walls under variable amplitude loading and strengthening reinforced concrete (RC) beams under monotonic static load has been carried out. In the reinforced masonry wall, cyclic fatigue stress has been applied to accelerate the static creep and to forecast the corresponding creep behaviour of masonry under static long-time loading. During the tests, the evaluation of fracture growth is monitored by coda wave interferometry which represents a novel approach in structural monitoring based on AE relative change velocity of coda signal. In general, the sensitivity of coda waves has been used to estimate velocity changes in fault zones, in volcanoes, in a mining environment, and in ultrasound experiments. This method uses multiple scattered waves, which travelled through the material along numerous paths, to infer tiny temporal changes in the wave velocity. The applied method has the potential to be used as a 'damage-gauge' for monitoring velocity changes as a sign of damage evolution into masonry and concrete structures.
Energy Technology Data Exchange (ETDEWEB)
Masera, D; Bocca, P; Grazzini, A, E-mail: davide.masera@polito.it [Department of Structural and Geotechnical Engineering - Politecnico di Torino, corso Duca degli Abruzzi 24, 10129 Turin (Italy)
2011-07-19
In this experimental program the main goal is to monitor the damage evolution in masonry and concrete structures by Acoustic Emission (AE) signal analysis applying a well-know seismic method. For this reason the concept of the coda wave interferometry is applied to AE signal recorded during the tests. Acoustic Emission (AE) are very effective non-destructive techniques applied to identify micro and macro-defects and their temporal evolution in several materials. This technique permits to estimate the velocity of ultrasound waves propagation and the amount of energy released during fracture propagation to obtain information on the criticality of the ongoing process. By means of AE monitoring, an experimental analysis on a set of reinforced masonry walls under variable amplitude loading and strengthening reinforced concrete (RC) beams under monotonic static load has been carried out. In the reinforced masonry wall, cyclic fatigue stress has been applied to accelerate the static creep and to forecast the corresponding creep behaviour of masonry under static long-time loading. During the tests, the evaluation of fracture growth is monitored by coda wave interferometry which represents a novel approach in structural monitoring based on AE relative change velocity of coda signal. In general, the sensitivity of coda waves has been used to estimate velocity changes in fault zones, in volcanoes, in a mining environment, and in ultrasound experiments. This method uses multiple scattered waves, which travelled through the material along numerous paths, to infer tiny temporal changes in the wave velocity. The applied method has the potential to be used as a 'damage-gauge' for monitoring velocity changes as a sign of damage evolution into masonry and concrete structures.
Dispersive photonic crystals from the plane wave method
Energy Technology Data Exchange (ETDEWEB)
Guevara-Cabrera, E.; Palomino-Ovando, M.A. [Facultad de Ciencias Físico Matemáticas, Benemérita Universidad Autónoma de Puebla, Apdo. Post. 165, Puebla, Pue. 72000, México (Mexico); Flores-Desirena, B., E-mail: bflores@fcfm.buap.mx [Facultad de Ciencias Físico Matemáticas, Benemérita Universidad Autónoma de Puebla, Apdo. Post. 165, Puebla, Pue. 72000, México (Mexico); Gaspar-Armenta, J.A. [Departamento de Investigación en Física de la Universidad de Sonora Apdo, Post 5-088, Hermosillo Sonora 83190, México (Mexico)
2016-03-01
Nowadays photonic crystals are widely used in many different applications. One of the most used methods to compute their band structure is the plane wave method (PWM). However, it can only be applied directly to non-dispersive media and be extended to systems with a few model dielectric functions. We explore an extension of the PWM to photonic crystals containing dispersive materials, that solves an eigenvalue equation for the Bloch wave vectors. First we compare our calculation with analytical results for one dimensional photonic crystals containing Si using experimental values of its optical parameters, and obtainig very well agreement, even for the spectrum region with strong absorption. Then, using the same method, we computed the band structure for a two dimensional photonic crystal without absorption, formed by an square array of MgO cylinders in air. The optical parameters for MgO were modeled with the Lorentz dielectric function. Finally, we studied an array of MgO cylinders in a metal, using Drude model without absorption, for the metal dielectric function. For this last case, we study the gap–midgap ratio as a function of the filling fraction for both the square and triangular lattice. The gap–midgap ratio is larger for the triangular lattice, with a maximum value of 10% for a filling fraction of 0.6. Our results show that the method can be applied to dispersive materials, and then to a wide range of applications where photonic crystals can be used.
An Analytical Method of Auxiliary Sources Solution for Plane Wave Scattering by Impedance Cylinders
DEFF Research Database (Denmark)
Larsen, Niels Vesterdal; Breinbjerg, Olav
2004-01-01
Analytical Method of Auxiliary Sources solutions for plane wave scattering by circular impedance cylinders are derived by transformation of the exact eigenfunction series solutions employing the Hankel function wave transformation. The analytical Method of Auxiliary Sources solution thus obtained...
Linear augmented plane wave method for self-consistent calculations
International Nuclear Information System (INIS)
Takeda, T.; Kuebler, J.
1979-01-01
O.K. Andersen has recently introduced a linear augmented plane wave method (LAPW) for the calculation of electronic structure that was shown to be computationally fast. A more general formulation of an LAPW method is presented here. It makes use of a freely disposable number of eigenfunctions of the radial Schroedinger equation. These eigenfunctions can be selected in a self-consistent way. The present formulation also results in a computationally fast method. It is shown that Andersen's LAPW is obtained in a special limit from the present formulation. Self-consistent test calculations for copper show the present method to be remarkably accurate. As an application, scalar-relativistic self-consistent calculations are presented for the band structure of FCC lanthanum. (author)
International Nuclear Information System (INIS)
Lee, Yong Suk; Ahn, Nam Sung
2010-01-01
Recently, the resource management of nuclear engineering manpower has become an important issue in Korean nuclear industry. The government's plan for increasing the number of domestic nuclear power plants and the recent success of nuclear power plant export to UAE (United Arab Emirates) will increase demand for nuclear engineers in Korea. Accordingly, the Korean government decided to supplement 2,246 engineers in the public sector of nuclear industry in the year 2010 to resolve the manpower shortage problem in the short term. However, the experienced engineers which are essentially important in the nuclear industry cannot be supplied in the short term. Therefore, development of the long term manpower demand forecast model of nuclear industry is needed. The system dynamics (SD) is useful method for forecasting nuclear manpower demand. It is because the time-delays which is important in constructing plants and in recruiting and training of engineers, and the feedback effect including the qualitative factor can be effectively considered in the SD method. Especially, the qualitative factor like 'Productivity' is very important concept in Human Resource Management (HRM) but it cannot be easily considered in the other methods. In this paper, the concepts of the nuclear manpower demand forecast model using the SD method are presented and the some simulation results are being discussed especially for the 'Operation Sector'
Energy Technology Data Exchange (ETDEWEB)
Lee, Yong Suk [Future and Challenges Inc., Seoul (Korea, Republic of); Ahn, Nam Sung [SolBridge International School of Business, Daejeon (Korea, Republic of)
2010-10-15
Recently, the resource management of nuclear engineering manpower has become an important issue in Korean nuclear industry. The government's plan for increasing the number of domestic nuclear power plants and the recent success of nuclear power plant export to UAE (United Arab Emirates) will increase demand for nuclear engineers in Korea. Accordingly, the Korean government decided to supplement 2,246 engineers in the public sector of nuclear industry in the year 2010 to resolve the manpower shortage problem in the short term. However, the experienced engineers which are essentially important in the nuclear industry cannot be supplied in the short term. Therefore, development of the long term manpower demand forecast model of nuclear industry is needed. The system dynamics (SD) is useful method for forecasting nuclear manpower demand. It is because the time-delays which is important in constructing plants and in recruiting and training of engineers, and the feedback effect including the qualitative factor can be effectively considered in the SD method. Especially, the qualitative factor like 'Productivity' is very important concept in Human Resource Management (HRM) but it cannot be easily considered in the other methods. In this paper, the concepts of the nuclear manpower demand forecast model using the SD method are presented and the some simulation results are being discussed especially for the 'Operation Sector'
Forecasting the Romanian sectoral economy using the input-output method
Directory of Open Access Journals (Sweden)
Liliana DUGULEANĂ
2017-07-01
Full Text Available The purpose of this paper is to forecast the sectoral output in 2013 based on the input-output structure of Romanian economy in 2010. Considering that the economic linkage mechanisms do not easily change during certain time periods, the forecasting is possible, even if not in the sequence of the time passing. Using the technical matrix of the sectoral structure described for year 2010 and some known indicators of the economic sectors, as the value added for each sector in 2013, the sectoral output is projected for 2013. The Romanian GDP in 2013 is estimated based on the input-output model. From a managerial perspective, this study is useful to forecast the sectoral output and to understand the sectoral behaviour, based on the input-output analysis of the value added, the compensation for employees and the final demand, which were considered here.
Directory of Open Access Journals (Sweden)
Cristhian Moreno-Chaparro
2011-12-01
Full Text Available This paper proposes a monthly electricity forecast method for the National Interconnected System (SIN of Colombia. The method preprocesses the time series using a Multiresolution Analysis (MRA with Discrete Wavelet Transform (DWT; a study for the selection of the mother wavelet and her order, as well as the level decomposition was carried out. Given that original series follows a non-linear behaviour, a neural nonlinear autoregressive (NAR model was used. The prediction was obtained by adding the forecast trend with the estimated obtained by the residual series combined with further components extracted from preprocessing. A bibliographic review of studies conducted internationally and in Colombia is included, in addition to references to investigations made with wavelet transform applied to electric energy prediction and studies reporting the use of NAR in prediction.
Liu, P.
2013-12-01
Quantitative analysis of the risk for reservoir real-time operation is a hard task owing to the difficulty of accurate description of inflow uncertainties. The ensemble-based hydrologic forecasts directly depict the inflows not only the marginal distributions but also their persistence via scenarios. This motivates us to analyze the reservoir real-time operating risk with ensemble-based hydrologic forecasts as inputs. A method is developed by using the forecast horizon point to divide the future time into two stages, the forecast lead-time and the unpredicted time. The risk within the forecast lead-time is computed based on counting the failure number of forecast scenarios, and the risk in the unpredicted time is estimated using reservoir routing with the design floods and the reservoir water levels of forecast horizon point. As a result, a two-stage risk analysis method is set up to quantify the entire flood risks by defining the ratio of the number of scenarios that excessive the critical value to the total number of scenarios. The China's Three Gorges Reservoir (TGR) is selected as a case study, where the parameter and precipitation uncertainties are implemented to produce ensemble-based hydrologic forecasts. The Bayesian inference, Markov Chain Monte Carlo, is used to account for the parameter uncertainty. Two reservoir operation schemes, the real operated and scenario optimization, are evaluated for the flood risks and hydropower profits analysis. With the 2010 flood, it is found that the improvement of the hydrologic forecast accuracy is unnecessary to decrease the reservoir real-time operation risk, and most risks are from the forecast lead-time. It is therefore valuable to decrease the avarice of ensemble-based hydrologic forecasts with less bias for a reservoir operational purpose.
Review of methods for forecasting the market penetration of new technologies
International Nuclear Information System (INIS)
Gilshannon, S.T.; Brown, D.R.
1996-12-01
In 1993 the DOE Office of Energy Efficiency and Renewable Energy (EE) initiated a program called Quality Metrics. Quality Metrics was developed to measure the costs and benefits of technologies being developed by EE R ampersand D programs. The impact of any new technology is directly related to its adoption by the market. The techniques employed to project market adoption are critical to measuring a new technology's impact. Our purpose was to review current market penetration theories and models and develop a recommended approach for evaluating the market penetration of DOE technologies. The following commonly cited innovation diffusion theories were reviewed to identify analytical approaches relevant to new energy technologies: (1) the normal noncumulative adopter distribution method, (2) the Bass Model, (3) the Mansfield-Blackman Model, (4) the Fisher-Pry Model, (5) a meta-analysis of innovation diffusion studies. Of the theories reviewed, the Bass and Mansfield-Blackman models were found most applicable to forecasting the market penetration of electricity supply technologies. Their algorithms require input estimates which characterize the technology adoption behavior of the electricity supply industry. But, inadequate work has been done to quantify the technology adoption characteristics of this industry. The following energy technology market penetration models were also reviewed: (1) DOE's Renewable Energy Penetration (REP) Model, (2) DOE's Electricity Capacity Planning Submodule of the National Energy Modeling System (NEMS), (3) the Assessment of Energy Technologies (ASSET) model by Regional Economic Research, Inc., (4) the Market TREK model by the Electric Power Research Institute (EPRI). The two DOE models were developed for electricity generation technologies whereas the Regional Economic Research and EPRI models were designed for demand- side energy technology markets. Therefore, the review and evaluation focused on the DOE models
Arsenault, R.; Mai, J.; Latraverse, M.; Tolson, B.
2017-12-01
Probabilistic ensemble forecasts generated by the ensemble streamflow prediction (ESP) methodology are subject to biases due to errors in the hydrological model's initial states. In day-to-day operations, hydrologists must compensate for discrepancies between observed and simulated states such as streamflow. However, in data-scarce regions, little to no information is available to guide the streamflow assimilation process. The manual assimilation process can then lead to more uncertainty due to the numerous options available to the forecaster. Furthermore, the model's mass balance may be compromised and could affect future forecasts. In this study we propose a data-driven approach in which specific variables that may be adjusted during assimilation are defined. The underlying principle was to identify key variables that would be the most appropriate to modify during streamflow assimilation depending on the initial conditions such as the time period of the assimilation, the snow water equivalent of the snowpack and meteorological conditions. The variables to adjust were determined by performing an automatic variational data assimilation on individual (or combinations of) model state variables and meteorological forcing. The assimilation aimed to simultaneously optimize: (1) the error between the observed and simulated streamflow at the timepoint where the forecasts starts and (2) the bias between medium to long-term observed and simulated flows, which were simulated by running the model with the observed meteorological data on a hindcast period. The optimal variables were then classified according to the initial conditions at the time period where the forecast is initiated. The proposed method was evaluated by measuring the average electricity generation of a hydropower complex in Québec, Canada driven by this method. A test-bed which simulates the real-world assimilation, forecasting, water release optimization and decision-making of a hydropower cascade was
Directory of Open Access Journals (Sweden)
Subanar Subanar
2006-01-01
Full Text Available Recently, one of the central topics for the neural networks (NN community is the issue of data preprocessing on the use of NN. In this paper, we will investigate this topic particularly on the effect of Decomposition method as data processing and the use of NN for modeling effectively time series with both trend and seasonal patterns. Limited empirical studies on seasonal time series forecasting with neural networks show that some find neural networks are able to model seasonality directly and prior deseasonalization is not necessary, and others conclude just the opposite. In this research, we study particularly on the effectiveness of data preprocessing, including detrending and deseasonalization by applying Decomposition method on NN modeling and forecasting performance. We use two kinds of data, simulation and real data. Simulation data are examined on multiplicative of trend and seasonality patterns. The results are compared to those obtained from the classical time series model. Our result shows that a combination of detrending and deseasonalization by applying Decomposition method is the effective data preprocessing on the use of NN for forecasting trend and seasonal time series.
Method of synthesizing silica nanofibers using sound waves
Sharma, Jaswinder K.; Datskos, Panos G.
2015-09-15
A method for synthesizing silica nanofibers using sound waves is provided. The method includes providing a solution of polyvinyl pyrrolidone, adding sodium citrate and ammonium hydroxide to form a first mixture, adding a silica-based compound to the solution to form a second mixture, and sonicating the second mixture to synthesize a plurality of silica nanofibers having an average cross-sectional diameter of less than 70 nm and having a length on the order of at least several hundred microns. The method can be performed without heating or electrospinning, and instead includes less energy intensive strategies that can be scaled up to an industrial scale. The resulting nanofibers can achieve a decreased mean diameter over conventional fibers. The decreased diameter generally increases the tensile strength of the silica nanofibers, as defects and contaminations decrease with the decreasing diameter.
A Comparison of Surface Acoustic Wave Modeling Methods
Wilson, W. c.; Atkinson, G. M.
2009-01-01
Surface Acoustic Wave (SAW) technology is low cost, rugged, lightweight, extremely low power and can be used to develop passive wireless sensors. For these reasons, NASA is investigating the use of SAW technology for Integrated Vehicle Health Monitoring (IVHM) of aerospace structures. To facilitate rapid prototyping of passive SAW sensors for aerospace applications, SAW models have been developed. This paper reports on the comparison of three methods of modeling SAWs. The three models are the Impulse Response Method a first order model, and two second order matrix methods; the conventional matrix approach, and a modified matrix approach that is extended to include internal finger reflections. The second order models are based upon matrices that were originally developed for analyzing microwave circuits using transmission line theory. Results from the models are presented with measured data from devices.
Liu, Dong-jun; Li, Li
2015-01-01
For the issue of haze-fog, PM2.5 is the main influence factor of haze-fog pollution in China. The trend of PM2.5 concentration was analyzed from a qualitative point of view based on mathematical models and simulation in this study. The comprehensive forecasting model (CFM) was developed based on the combination forecasting ideas. Autoregressive Integrated Moving Average Model (ARIMA), Artificial Neural Networks (ANNs) model and Exponential Smoothing Method (ESM) were used to predict the time series data of PM2.5 concentration. The results of the comprehensive forecasting model were obtained by combining the results of three methods based on the weights from the Entropy Weighting Method. The trend of PM2.5 concentration in Guangzhou China was quantitatively forecasted based on the comprehensive forecasting model. The results were compared with those of three single models, and PM2.5 concentration values in the next ten days were predicted. The comprehensive forecasting model balanced the deviation of each single prediction method, and had better applicability. It broadens a new prediction method for the air quality forecasting field. PMID:26110332
International Nuclear Information System (INIS)
Valeo, Ernest; Johnson, Jay R.; Kim, Eun-Hwa; Phillips, Cynthia
2012-01-01
A wide variety of plasma waves play an important role in the energization and loss of particles in the inner magnetosphere. Our ability to understand and model wave-particle interactions in this region requires improved knowledge of the spatial distribution and properties of these waves as well as improved understanding of how the waves depend on changes in solar wind forcing and/or geomagnetic activity. To this end, we have developed a two-dimensional, finite element code that solves the full wave equations in global magnetospheric geometry. The code describes three-dimensional wave structure including mode conversion when ULF, EMIC, and whistler waves are launched in a two-dimensional axisymmetric background plasma with general magnetic field topology. We illustrate the capabilities of the code by examining the role of plasmaspheric plumes on magnetosonic wave propagation; mode conversion at the ion-ion and Alfven resonances resulting from external, solar wind compressions; and wave structure and mode conversion of electromagnetic ion cyclotron waves launched in the equatorial magnetosphere, which propagate along the magnetic field lines toward the ionosphere. We also discuss advantages of the finite element method for resolving resonant structures, and how the model may be adapted to include nonlocal kinetic effects.
A numerical method for determining the radial wave motion correction in plane wave couplers
DEFF Research Database (Denmark)
Cutanda Henriquez, Vicente; Barrera Figueroa, Salvador; Torras Rosell, Antoni
2016-01-01
Microphones are used for realising the unit of sound pressure level, the pascal (Pa). Electro-acoustic reciprocity is the preferred method for the absolute determination of the sensitivity. This method can be applied in different sound fields: uniform pressure, free field or diffuse field. Pressure...... solution is an analytical expression that estimates the difference between the ideal plane wave sound field and a more complex lossless sound field created by a non-planar movement of the microphone’s membranes. Alternatively, a correction may be calculated numerically by introducing a full model...... of the microphone-coupler system in a Boundary Element formulation. In order to obtain a realistic representation of the sound field, viscous losses must be introduced in the model. This paper presents such a model, and the results of the simulations for different combinations of microphones and couplers...
Seiffert, Betsy R.; Ducrozet, Guillaume; Bonnefoy, Félicien
2017-11-01
This study investigates a wave-breaking onset criteria to be implemented in the non-linear potential flow solver HOS-NWT. The model is a computationally efficient, open source code, which solves for the free surface in a numerical wave tank using the High-Order Spectral (HOS) method. The goal of this study is to determine the best method to identify the onset of random single and multiple breaking waves over a large domain at the exact time they occur. To identify breaking waves, a breaking onset criteria based on the ratio of local energy flux velocity to the local crest velocity, introduced by Barthelemy et al. (2017) is selected. The breaking parameter is uniquely applied in the numerical model in that calculations of the breaking onset criteria ratio are not made only at the location of the wave crest, but at every point in the domain and at every time step. This allows the model to calculate the onset of a breaking wave the moment it happens, and without knowing anything about the wave a priori. The application of the breaking criteria at every point in the domain and at every time step requires the phase velocity to be calculated instantaneously everywhere in the domain and at every time step. This is achieved by calculating the instantaneous phase velocity using the Hilbert transform and dispersion relation. A comparison between more traditional crest-tracking techniques shows the calculation of phase velocity using Hilbert transform at the location of the breaking wave crest provides a good approximation of crest velocity. The ability of the selected wave breaking criteria to predict single and multiple breaking events in two dimensions is validated by a series of large-scale experiments. Breaking waves are generated by energy focusing and modulational instability methods, with a wide range of primary frequencies. Steep irregular waves which lead to breaking waves, and irregular waves with an energy focusing wave superimposed are also generated. This set of
Time evolution of the wave equation using rapid expansion method
Pestana, Reynam C.; Stoffa, Paul L.
2010-01-01
Forward modeling of seismic data and reverse time migration are based on the time evolution of wavefields. For the case of spatially varying velocity, we have worked on two approaches to evaluate the time evolution of seismic wavefields. An exact solution for the constant-velocity acoustic wave equation can be used to simulate the pressure response at any time. For a spatially varying velocity, a one-step method can be developed where no intermediate time responses are required. Using this approach, we have solved for the pressure response at intermediate times and have developed a recursive solution. The solution has a very high degree of accuracy and can be reduced to various finite-difference time-derivative methods, depending on the approximations used. Although the two approaches are closely related, each has advantages, depending on the problem being solved. © 2010 Society of Exploration Geophysicists.
Analytic moment method calculations of the drift wave spectrum
International Nuclear Information System (INIS)
Thayer, D.R.; Molvig, K.
1985-11-01
A derivation and approximate solution of renormalized mode coupling equations describing the turbulent drift wave spectrum is presented. Arguments are given which indicate that a weak turbulence formulation of the spectrum equations fails for a system with negative dissipation. The inadequacy of the weak turbulence theory is circumvented by utilizing a renormalized formation. An analytic moment method is developed to approximate the solution of the nonlinear spectrum integral equations. The solution method employs trial functions to reduce the integral equations to algebraic equations in basic parameters describing the spectrum. An approximate solution of the spectrum equations is first obtained for a mode dissipation with known solution, and second for an electron dissipation in the NSA
Dominant partition method. [based on a wave function formalism
Dixon, R. M.; Redish, E. F.
1979-01-01
By use of the L'Huillier, Redish, and Tandy (LRT) wave function formalism, a partially connected method, the dominant partition method (DPM) is developed for obtaining few body reductions of the many body problem in the LRT and Bencze, Redish, and Sloan (BRS) formalisms. The DPM maps the many body problem to a fewer body one by using the criterion that the truncated formalism must be such that consistency with the full Schroedinger equation is preserved. The DPM is based on a class of new forms for the irreducible cluster potential, which is introduced in the LRT formalism. Connectivity is maintained with respect to all partitions containing a given partition, which is referred to as the dominant partition. Degrees of freedom corresponding to the breakup of one or more of the clusters of the dominant partition are treated in a disconnected manner. This approach for simplifying the complicated BRS equations is appropriate for physical problems where a few body reaction mechanism prevails.
Time evolution of the wave equation using rapid expansion method
Pestana, Reynam C.
2010-07-01
Forward modeling of seismic data and reverse time migration are based on the time evolution of wavefields. For the case of spatially varying velocity, we have worked on two approaches to evaluate the time evolution of seismic wavefields. An exact solution for the constant-velocity acoustic wave equation can be used to simulate the pressure response at any time. For a spatially varying velocity, a one-step method can be developed where no intermediate time responses are required. Using this approach, we have solved for the pressure response at intermediate times and have developed a recursive solution. The solution has a very high degree of accuracy and can be reduced to various finite-difference time-derivative methods, depending on the approximations used. Although the two approaches are closely related, each has advantages, depending on the problem being solved. © 2010 Society of Exploration Geophysicists.
DEFF Research Database (Denmark)
Ambühl, Simon; Kramer, Morten Mejlhede; Sørensen, John Dalsgaard
2016-01-01
Inspection and maintenance costs are significant contributors to the cost of energy for wave energy converters. Maintenance can be performed after failure (corrective) or before a breakdown (preventive) occurs. Furthermore, helicopter and boat can be used to transport equipment and personnel to t...
Aiolfi, Marco; Capistrán, Carlos; Timmermann, Allan
2010-01-01
We consider combinations of subjective survey forecasts and model-based forecasts from linear and non-linear univariate specifications as well as multivariate factor-augmented models. Empirical results suggest that a simple equal-weighted average of survey forecasts outperform the best model-based forecasts for a majority of macroeconomic variables and forecast horizons. Additional improvements can in some cases be gained by using a simple equal-weighted average of survey and model-based fore...
Determination of optimum "multi-channel surface wave method" field parameters.
2012-12-01
Multi-channel surface wave methods (especially the multi-channel analyses of surface wave method; MASW) are routinely used to : determine the shear-wave velocity of the subsurface to depths of 100 feet for site classification purposes. Users are awar...
Seiffert, Betsy R.; Ducrozet, Guillaume
2018-01-01
We examine the implementation of a wave-breaking mechanism into a nonlinear potential flow solver. The success of the mechanism will be studied by implementing it into the numerical model HOS-NWT, which is a computationally efficient, open source code that solves for the free surface in a numerical wave tank using the high-order spectral (HOS) method. Once the breaking mechanism is validated, it can be implemented into other nonlinear potential flow models. To solve for wave-breaking, first a wave-breaking onset parameter is identified, and then a method for computing wave-breaking associated energy loss is determined. Wave-breaking onset is calculated using a breaking criteria introduced by Barthelemy et al. (J Fluid Mech https://arxiv.org/pdf/1508.06002.pdf, submitted) and validated with the experiments of Saket et al. (J Fluid Mech 811:642-658, 2017). Wave-breaking energy dissipation is calculated by adding a viscous diffusion term computed using an eddy viscosity parameter introduced by Tian et al. (Phys Fluids 20(6): 066,604, 2008, Phys Fluids 24(3), 2012), which is estimated based on the pre-breaking wave geometry. A set of two-dimensional experiments is conducted to validate the implemented wave breaking mechanism at a large scale. Breaking waves are generated by using traditional methods of evolution of focused waves and modulational instability, as well as irregular breaking waves with a range of primary frequencies, providing a wide range of breaking conditions to validate the solver. Furthermore, adjustments are made to the method of application and coefficient of the viscous diffusion term with negligible difference, supporting the robustness of the eddy viscosity parameter. The model is able to accurately predict surface elevation and corresponding frequency/amplitude spectrum, as well as energy dissipation when compared with the experimental measurements. This suggests the model is capable of calculating wave-breaking onset and energy dissipation
Ike, P.; Voogd, Henk; Voogd, Henk; Linden, Gerard
2004-01-01
This chapter begins with a discussion of qualitative forecasting by describing a number of methods that depend on judgements made by stakeholders, experts or other interested parties to arrive at forecasts. Two qualitative approaches are illuminated, the Delphi and scenario methods respectively.
Supersonic wave detection method and supersonic detection device
International Nuclear Information System (INIS)
Machida, Koichi; Seto, Takehiro; Ishizaki, Hideaki; Asano, Rin-ichi.
1996-01-01
The present invention provides a method of and device for a detection suitable to a channel box which is used while covering a fuel assembly of a BWR type reactor. Namely, a probe for transmitting/receiving supersonic waves scans on the surface of the channel box. A data processing device determines an index showing a selective orientation degree of crystal direction of the channel box based on the signals received by the probe. A judging device compares the determined index with a previously determined allowable range to judge whether the channel box is satisfactory or not based on the result of the comparison. The judgement are on the basis that (1) the bending of the channel box is caused by the difference of elongation of opposed surfaces, (2) the elongation due to irradiation is caused by the selective orientation of crystal direction, and (3) the bending of the channel box can be suppressed within a predetermined range by suppressing the index determined by the measurement of supersonic waves having a correlation with the selective orientation of the crystal direction. As a result, the performance of the channel box capable of enduring high burnup region can be confirmed in a nondestructive manner. (I.S.)
A stochastic collocation method for the second order wave equation with a discontinuous random speed
Motamed, Mohammad; Nobile, Fabio; Tempone, Raul
2012-01-01
In this paper we propose and analyze a stochastic collocation method for solving the second order wave equation with a random wave speed and subjected to deterministic boundary and initial conditions. The speed is piecewise smooth in the physical
Tienfuan Kerh; Shin-Hung Wu
2017-01-01
Forecasting of a typhoon moving path may help to evaluate the potential negative impacts in the neighbourhood areas along the moving path. This study proposed a work of using both static and dynamic neural network models to link a time series of typhoon track parameters including longitude and latitude of the typhoon central location, cyclonic radius, central wind speed, and typhoon moving speed. Based on the historical records of 100 typhoons, the performances of neural network models are ev...
Horvitz, Eric J.; Apacible, Johnson; Sarin, Raman; Liao, Lin
2012-01-01
We present research on developing models that forecast traffic flow and congestion in the Greater Seattle area. The research has led to the deployment of a service named JamBayes, that is being actively used by over 2,500 users via smartphones and desktop versions of the system. We review the modeling effort and describe experiments probing the predictive accuracy of the models. Finally, we present research on building models that can identify current and future surprises, via efforts on mode...
Forecast daily indices of solar activity, F10.7, using support vector regression method
International Nuclear Information System (INIS)
Huang Cong; Liu Dandan; Wang Jingsong
2009-01-01
The 10.7 cm solar radio flux (F10.7), the value of the solar radio emission flux density at a wavelength of 10.7 cm, is a useful index of solar activity as a proxy for solar extreme ultraviolet radiation. It is meaningful and important to predict F10.7 values accurately for both long-term (months-years) and short-term (days) forecasting, which are often used as inputs in space weather models. This study applies a novel neural network technique, support vector regression (SVR), to forecasting daily values of F10.7. The aim of this study is to examine the feasibility of SVR in short-term F10.7 forecasting. The approach, based on SVR, reduces the dimension of feature space in the training process by using a kernel-based learning algorithm. Thus, the complexity of the calculation becomes lower and a small amount of training data will be sufficient. The time series of F10.7 from 2002 to 2006 are employed as the data sets. The performance of the approach is estimated by calculating the norm mean square error and mean absolute percentage error. It is shown that our approach can perform well by using fewer training data points than the traditional neural network. (research paper)
Tsunami Forecasting in the Atlantic Basin
Knight, W. R.; Whitmore, P.; Sterling, K.; Hale, D. A.; Bahng, B.
2012-12-01
The mission of the West Coast and Alaska Tsunami Warning Center (WCATWC) is to provide advance tsunami warning and guidance to coastal communities within its Area-of-Responsibility (AOR). Predictive tsunami models, based on the shallow water wave equations, are an important part of the Center's guidance support. An Atlantic-based counterpart to the long-standing forecasting ability in the Pacific known as the Alaska Tsunami Forecast Model (ATFM) is now developed. The Atlantic forecasting method is based on ATFM version 2 which contains advanced capabilities over the original model; including better handling of the dynamic interactions between grids, inundation over dry land, new forecast model products, an optional non-hydrostatic approach, and the ability to pre-compute larger and more finely gridded regions using parallel computational techniques. The wide and nearly continuous Atlantic shelf region presents a challenge for forecast models. Our solution to this problem has been to develop a single unbroken high resolution sub-mesh (currently 30 arc-seconds), trimmed to the shelf break. This allows for edge wave propagation and for kilometer scale bathymetric feature resolution. Terminating the fine mesh at the 2000m isobath keeps the number of grid points manageable while allowing for a coarse (4 minute) mesh to adequately resolve deep water tsunami dynamics. Higher resolution sub-meshes are then included around coastal forecast points of interest. The WCATWC Atlantic AOR includes eastern U.S. and Canada, the U.S. Gulf of Mexico, Puerto Rico, and the Virgin Islands. Puerto Rico and the Virgin Islands are in very close proximity to well-known tsunami sources. Because travel times are under an hour and response must be immediate, our focus is on pre-computing many tsunami source "scenarios" and compiling those results into a database accessible and calibrated with observations during an event. Seismic source evaluation determines the order of model pre
International Nuclear Information System (INIS)
Jin, Cheng Hao; Pok, Gouchol; Lee, Yongmi; Park, Hyun-Woo; Kim, Kwang Deuk; Yun, Unil; Ryu, Keun Ho
2015-01-01
Highlights: • A novel pattern sequence-based direct time series forecasting method was proposed. • Due to the use of SOM’s topology preserving property, only SOM can be applied. • SCPSNSP only deals with the cluster patterns not each specific time series value. • SCPSNSP performs better than recently developed forecasting algorithms. - Abstract: In this paper, we propose a new day-ahead direct time series forecasting method for competitive electricity markets based on clustering and next symbol prediction. In the clustering step, pattern sequence and their topology relations are obtained from self organizing map time series clustering. In the next symbol prediction step, with each cluster label in the pattern sequence represented as a pair of its topologically identical coordinates, artificial neural network is used to predict the topological coordinates of next day by training the relationship between previous daily pattern sequence and its next day pattern. According to the obtained topology relations, the nearest nonzero hits pattern is assigned to next day so that the whole time series values can be directly forecasted from the assigned cluster pattern. The proposed method was evaluated on Spanish, Australian and New York electricity markets and compared with PSF and some of the most recently published forecasting methods. Experimental results show that the proposed method outperforms the best forecasting methods at least 3.64%
The Ion Cyclotron, Lower Hybrid, and Alfven Wave Heating Methods
International Nuclear Information System (INIS)
Koch, R.
2004-01-01
This lecture covers the practical features and experimental results of the three heating methods. The emphasis is on ion cyclotron heating. First, we briefly come back to the main non-collisional heating mechanisms and to the particular features of the quasilinear coefficient in the ion cyclotron range of frequencies (ICRF). The specific case of the ion-ion hybrid resonance is treated, as well as the polarisation issue and minority heating scheme. The various ICRF scenarios are reviewed. The experimental applications of ion cyclotron resonance heating (ICRH) systems are outlined. Then, the lower hybrid and Alfven wave heating and current drive experimental results are covered more briefly. Where applicable, the prospects for ITER are commented
Directory of Open Access Journals (Sweden)
Yi Liang
2016-11-01
Full Text Available The power industry is the main battlefield of CO2 emission reduction, which plays an important role in the implementation and development of the low carbon economy. The forecasting of electricity demand can provide a scientific basis for the country to formulate a power industry development strategy and further promote the sustained, healthy and rapid development of the national economy. Under the goal of low-carbon economy, medium and long term electricity demand forecasting will have very important practical significance. In this paper, a new hybrid electricity demand model framework is characterized as follows: firstly, integration of grey relation degree (GRD with induced ordered weighted harmonic averaging operator (IOWHA to propose a new weight determination method of hybrid forecasting model on basis of forecasting accuracy as induced variables is presented; secondly, utilization of the proposed weight determination method to construct the optimal hybrid forecasting model based on extreme learning machine (ELM forecasting model and multiple regression (MR model; thirdly, three scenarios in line with the level of realization of various carbon emission targets and dynamic simulation of effect of low-carbon economy on future electricity demand are discussed. The resulting findings show that, the proposed model outperformed and concentrated some monomial forecasting models, especially in boosting the overall instability dramatically. In addition, the development of a low-carbon economy will increase the demand for electricity, and have an impact on the adjustment of the electricity demand structure.
Anggraeni, Novia Antika
2015-04-01
The test of eruption time prediction is an effort to prepare volcanic disaster mitigation, especially in the volcano's inhabited slope area, such as Merapi Volcano. The test can be conducted by observing the increase of volcanic activity, such as seismicity degree, deformation and SO2 gas emission. One of methods that can be used to predict the time of eruption is Materials Failure Forecast Method (FFM). Materials Failure Forecast Method (FFM) is a predictive method to determine the time of volcanic eruption which was introduced by Voight (1988). This method requires an increase in the rate of change, or acceleration of the observed volcanic activity parameters. The parameter used in this study is the seismic energy value of Merapi Volcano from 1990 - 2012. The data was plotted in form of graphs of seismic energy rate inverse versus time with FFM graphical technique approach uses simple linear regression. The data quality control used to increase the time precision employs the data correlation coefficient value of the seismic energy rate inverse versus time. From the results of graph analysis, the precision of prediction time toward the real time of eruption vary between -2.86 up to 5.49 days.
International Nuclear Information System (INIS)
Anggraeni, Novia Antika
2015-01-01
The test of eruption time prediction is an effort to prepare volcanic disaster mitigation, especially in the volcano’s inhabited slope area, such as Merapi Volcano. The test can be conducted by observing the increase of volcanic activity, such as seismicity degree, deformation and SO2 gas emission. One of methods that can be used to predict the time of eruption is Materials Failure Forecast Method (FFM). Materials Failure Forecast Method (FFM) is a predictive method to determine the time of volcanic eruption which was introduced by Voight (1988). This method requires an increase in the rate of change, or acceleration of the observed volcanic activity parameters. The parameter used in this study is the seismic energy value of Merapi Volcano from 1990 – 2012. The data was plotted in form of graphs of seismic energy rate inverse versus time with FFM graphical technique approach uses simple linear regression. The data quality control used to increase the time precision employs the data correlation coefficient value of the seismic energy rate inverse versus time. From the results of graph analysis, the precision of prediction time toward the real time of eruption vary between −2.86 up to 5.49 days
Energy Technology Data Exchange (ETDEWEB)
Anggraeni, Novia Antika, E-mail: novia.antika.a@gmail.com [Geophysics Sub-department, Physics Department, Faculty of Mathematic and Natural Science, Universitas Gadjah Mada. BLS 21 Yogyakarta 55281 (Indonesia)
2015-04-24
The test of eruption time prediction is an effort to prepare volcanic disaster mitigation, especially in the volcano’s inhabited slope area, such as Merapi Volcano. The test can be conducted by observing the increase of volcanic activity, such as seismicity degree, deformation and SO2 gas emission. One of methods that can be used to predict the time of eruption is Materials Failure Forecast Method (FFM). Materials Failure Forecast Method (FFM) is a predictive method to determine the time of volcanic eruption which was introduced by Voight (1988). This method requires an increase in the rate of change, or acceleration of the observed volcanic activity parameters. The parameter used in this study is the seismic energy value of Merapi Volcano from 1990 – 2012. The data was plotted in form of graphs of seismic energy rate inverse versus time with FFM graphical technique approach uses simple linear regression. The data quality control used to increase the time precision employs the data correlation coefficient value of the seismic energy rate inverse versus time. From the results of graph analysis, the precision of prediction time toward the real time of eruption vary between −2.86 up to 5.49 days.
A simplified method of evaluating the stress wave environment of internal equipment
Colton, J. D.; Desmond, T. P.
1979-01-01
A simplified method called the transfer function technique (TFT) was devised for evaluating the stress wave environment in a structure containing internal equipment. The TFT consists of following the initial in-plane stress wave that propagates through a structure subjected to a dynamic load and characterizing how the wave is altered as it is transmitted through intersections of structural members. As a basis for evaluating the TFT, impact experiments and detailed stress wave analyses were performed for structures with two or three, or more members. Transfer functions that relate the wave transmitted through an intersection to the incident wave were deduced from the predicted wave response. By sequentially applying these transfer functions to a structure with several intersections, it was found that the environment produced by the initial stress wave propagating through the structure can be approximated well. The TFT can be used as a design tool or as an analytical tool to determine whether a more detailed wave analysis is warranted.
Character of GPR wave in air and processed method
International Nuclear Information System (INIS)
Shi Jianping; Zhang Zhiyong; Deng Juzhi
2009-01-01
The wave reflected by objects in the air is unavoidable because electromagnetic wave of GPR was send to all directions. There are three air reflection types: directly arrived wave, system ring and reflection wave. The directly arrived waves don't disturb the recognition of the reflections from earth because they affect the first short time of GPR trace record. But system ring and reflection from air are the mainly part of disturbs. The time and distance curve of reflection from air can be classified into two types: hyperbola type and line type. The reflection from air and from earth can be recognized by calculating the velocity of electromagnetic wave. Line type reflection can be filtered by background remove and 2-D filter; by comparing the migrated profiles with velocity in air and ground, the interpretation will become more exact. (authors)
Air Pollution Forecasts: An Overview
Bai, Lu; Wang, Jianzhou; Lu, Haiyan
2018-01-01
Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies. PMID:29673227
Air Pollution Forecasts: An Overview.
Bai, Lu; Wang, Jianzhou; Ma, Xuejiao; Lu, Haiyan
2018-04-17
Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies.
Air Pollution Forecasts: An Overview
Directory of Open Access Journals (Sweden)
Lu Bai
2018-04-01
Full Text Available Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies.
International Nuclear Information System (INIS)
Zhong Jian; Dong Gang; Sun Yimei; Zhang Zhaoyang; Wu Yuqin
2016-01-01
The present work reports the development of nonlinear time series prediction method of genetic algorithm (GA) with singular spectrum analysis (SSA) for forecasting the surface wind of a point station in the South China Sea (SCS) with scatterometer observations. Before the nonlinear technique GA is used for forecasting the time series of surface wind, the SSA is applied to reduce the noise. The surface wind speed and surface wind components from scatterometer observations at three locations in the SCS have been used to develop and test the technique. The predictions have been compared with persistence forecasts in terms of root mean square error. The predicted surface wind with GA and SSA made up to four days (longer for some point station) in advance have been found to be significantly superior to those made by persistence model. This method can serve as a cost-effective alternate prediction technique for forecasting surface wind of a point station in the SCS basin. (paper)
International Nuclear Information System (INIS)
Leaci, Paola
2015-01-01
Semicoherent all-sky searches over year-long observation times for continuous gravitational wave signals produce various thousands of potential periodic source candidates. Efficient methods able to discard false candidate events are crucial in order to put all the efforts into a computationally intensive follow-up analysis for the remaining most promising candidates (Shaltev et al 2014 Phys. Rev. D 89 124030). In this paper we present a set of techniques able to fulfill such requirements, identifying and eliminating false candidate events, reducing thus the bulk of candidate sets that need to be further investigated. Some of these techniques were also used to streamline the candidate sets returned by the Einstein@Home hierarchical searches presented in (Aasi J et al (The LIGO Scientific Collaboration and the Virgo Collaboration) 2013 Phys. Rev. D 87 042001). These powerful methods and the benefits originating from their application to both simulated and on detector data from the fifth LIGO science run are illustrated and discussed. (paper)
1981-01-01
for the third and fourth day precipitation forecasts. A marked improvement was shown for the consensus 24 hour precipitation forecast, and small... Zuckerberg (1980) found a small long term skill increase in forecasts of heavy snow events for nine eastern cities. Other National Weather Service...and maximum temperature) are each awarded marks 2, 1, or 0 according to whether the forecast is correct, 8 - *- -**■*- ———"—- - -■ t0m 1 MM—IB I
Expansion and compression shock wave calculation in pipes with the C.V.M. numerical method
International Nuclear Information System (INIS)
Raymond, P.; Caumette, P.; Le Coq, G.; Libmann, M.
1983-03-01
The Control Variables Method for fluid transients computations has been used to compute expansion and compression shock waves propagations. In this paper, first analytical solutions for shock wave and rarefaction wave propagation are detailed. Then after a rapid description of the C.V.M. technique and its stability and monotonicity properties, we will present some results about standard shock tube problem, reflection of shock wave, finally a comparison between experimental results obtained on the ELF facility and calculations is given
Developments in radar and remote-sensing methods for measuring and forecasting rainfall.
Collier, C G
2002-07-15
Over the last 25 years or so, weather-radar networks have become an integral part of operational meteorological observing systems. While measurements of rainfall made using radar systems have been used qualitatively by weather forecasters, and by some operational hydrologists, acceptance has been limited as a consequence of uncertainties in the quality of the data. Nevertheless, new algorithms for improving the accuracy of radar measurements of rainfall have been developed, including the potential to calibrate radars using the measurements of attenuation on microwave telecommunications links. Likewise, ways of assimilating these data into both meteorological and hydrological models are being developed. In this paper we review the current accuracy of radar estimates of rainfall, pointing out those approaches to the improvement of accuracy which are likely to be most successful operationally. Comment is made on the usefulness of satellite data for estimating rainfall in a flood-forecasting context. Finally, problems in coping with the error characteristics of all these data using both simple schemes and more complex four-dimensional variational analysis are being addressed, and are discussed briefly in this paper.
Spatial electric load forecasting
Willis, H Lee
2002-01-01
Spatial Electric Load Forecasting Consumer Demand for Power and ReliabilityCoincidence and Load BehaviorLoad Curve and End-Use ModelingWeather and Electric LoadWeather Design Criteria and Forecast NormalizationSpatial Load Growth BehaviorSpatial Forecast Accuracy and Error MeasuresTrending MethodsSimulation Method: Basic ConceptsA Detailed Look at the Simulation MethodBasics of Computerized SimulationAnalytical Building Blocks for Spatial SimulationAdvanced Elements of Computerized SimulationHybrid Trending-Simulation MethodsAdvanced
The (′/-Expansion Method for Abundant Traveling Wave Solutions of Caudrey-Dodd-Gibbon Equation
Directory of Open Access Journals (Sweden)
Hasibun Naher
2011-01-01
Full Text Available We construct the traveling wave solutions of the fifth-order Caudrey-Dodd-Gibbon (CDG equation by the (/-expansion method. Abundant traveling wave solutions with arbitrary parameters are successfully obtained by this method and the wave solutions are expressed in terms of the hyperbolic, the trigonometric, and the rational functions. It is shown that the (/-expansion method is a powerful and concise mathematical tool for solving nonlinear partial differential equations.
A novel method for predicting the power outputs of wave energy converters
Wang, Yingguang
2018-03-01
This paper focuses on realistically predicting the power outputs of wave energy converters operating in shallow water nonlinear waves. A heaving two-body point absorber is utilized as a specific calculation example, and the generated power of the point absorber has been predicted by using a novel method (a nonlinear simulation method) that incorporates a second order random wave model into a nonlinear dynamic filter. It is demonstrated that the second order random wave model in this article can be utilized to generate irregular waves with realistic crest-trough asymmetries, and consequently, more accurate generated power can be predicted by subsequently solving the nonlinear dynamic filter equation with the nonlinearly simulated second order waves as inputs. The research findings demonstrate that the novel nonlinear simulation method in this article can be utilized as a robust tool for ocean engineers in their design, analysis and optimization of wave energy converters.
Chase, Valerie J; Cohn, Amy E M; Peterson, Timothy A; Lavieri, Mariel S
2012-05-01
This study investigated whether emergency department (ED) variables could be used in mathematical models to predict a future surge in ED volume based on recent levels of use of physician capacity. The models may be used to guide decisions related to on-call staffing in non-crisis-related surges of patient volume. A retrospective analysis was conducted using information spanning July 2009 through June 2010 from a large urban teaching hospital with a Level I trauma center. A comparison of significance was used to assess the impact of multiple patient-specific variables on the state of the ED. Physician capacity was modeled based on historical physician treatment capacity and productivity. Binary logistic regression analysis was used to determine the probability that the available physician capacity would be sufficient to treat all patients forecasted to arrive in the next time period. The prediction horizons used were 15 minutes, 30 minutes, 1 hour, 2 hours, 4 hours, 8 hours, and 12 hours. Five consecutive months of patient data from July 2010 through November 2010, similar to the data used to generate the models, was used to validate the models. Positive predictive values, Type I and Type II errors, and real-time accuracy in predicting noncrisis surge events were used to evaluate the forecast accuracy of the models. The ratio of new patients requiring treatment over total physician capacity (termed the care utilization ratio [CUR]) was deemed a robust predictor of the state of the ED (with a CUR greater than 1 indicating that the physician capacity would not be sufficient to treat all patients forecasted to arrive). Prediction intervals of 30 minutes, 8 hours, and 12 hours performed best of all models analyzed, with deviances of 1.000, 0.951, and 0.864, respectively. A 95% significance was used to validate the models against the July 2010 through November 2010 data set. Positive predictive values ranged from 0.738 to 0.872, true positives ranged from 74% to 94%, and
A hybrid method for forecasting the energy output of photovoltaic systems
International Nuclear Information System (INIS)
Ramsami, Pamela; Oree, Vishwamitra
2015-01-01
Highlights: • We propose a novel hybrid technique for predicting the daily PV energy output. • Multiple linear regression, FFNN and GRNN artificial neural networks are used. • Stepwise regression is used to select the most relevant meteorological parameters. • SR-FFNN reduces the average dispersion and overall bias in prediction errors. • Accuracy metrics of hybrid models are better than those of single-stage models. - Abstract: The intermittent nature of solar energy poses many challenges to renewable energy system operators in terms of operational planning and scheduling. Predicting the output of photovoltaic systems is therefore essential for managing the operation and assessing the economic performance of power systems. This paper presents a new technique for forecasting the 24-h ahead stochastic energy output of photovoltaic systems based on the daily weather forecasts. A comparison of the performances of the hybrid technique with conventional linear regression and artificial neural network models has also been reported. Initially, three single-stage models were designed, namely the generalized regression neural network, feedforward neural network and multiple linear regression. Subsequently, a hybrid-modeling approach was adopted by applying stepwise regression to select input variables of greater importance. These variables were then fed to the single-stage models resulting in three hybrid models. They were then validated by comparing the forecasts of the models with measured dataset from an operational photovoltaic system. The accuracy of the each model was evaluated based on the correlation coefficient, mean absolute error, mean bias error and root mean square error values. Simulation results revealed that the hybrid models perform better than their corresponding single-stage models. Stepwise regression-feedforward neural network hybrid model outperformed the other models with root mean square error, mean absolute error, mean bias error and
Directory of Open Access Journals (Sweden)
Jonas W. Pedersen
2016-09-01
Full Text Available High quality on-line flow forecasts are useful for real-time operation of urban drainage systems and wastewater treatment plants. This requires computationally efficient models, which are continuously updated with observed data to provide good initial conditions for the forecasts. This paper presents a way of updating conceptual rainfall-runoff models using Maximum a Posteriori estimation to determine the most likely parameter constellation at the current point in time. This is done by combining information from prior parameter distributions and the model goodness of fit over a predefined period of time that precedes the forecast. The method is illustrated for an urban catchment, where flow forecasts of 0–4 h are generated by applying a lumped linear reservoir model with three cascading reservoirs. Radar rainfall observations are used as input to the model. The effects of different prior standard deviations and lengths of the auto-calibration period on the resulting flow forecast performance are evaluated. We were able to demonstrate that, if properly tuned, the method leads to a significant increase in forecasting performance compared to a model without continuous auto-calibration. Delayed responses and erratic behaviour in the parameter variations are, however, observed and the choice of prior distributions and length of auto-calibration period is not straightforward.
Levshin, Anatoli L.; Barmin, Mikhail P.; Moschetti, Morgan P.; Mendoza, Carlos; Ritzwoller, Michael H.
2012-01-01
The purpose of this study is to develop and test a modiﬁcation to a previous method of regional seismic event location based on Empirical Green’s Functions (EGFs) produced from ambient seismic noise. Elastic EGFs between pairs of seismic stations are determined by cross-correlating long ambient noise time-series recorded at the two stations. The EGFs principally contain Rayleigh- and Love-wave energy on the vertical and transverse components, respectively, and we utilize these signals between about 5 and 12 s period. The previous method, based exclusively on Rayleigh waves, may yield biased epicentral locations for certain event types with hypocentral depths between 2 and 5 km. Here we present theoretical arguments that show how Love waves can be introduced to reduce or potentially eliminate the bias. We also present applications of Rayleigh- and Love-wave EGFs to locate 10 reference events in the western United States. The separate Rayleigh and Love epicentral locations and the joint locations using a combination of the two waves agree to within 1 km distance, on average, but conﬁdence ellipses are smallest when both types of waves are used.
New forecasting methods of the intensity and time development of geomagnetic and ionospheric storms
International Nuclear Information System (INIS)
Akasofu, S.I.
1981-01-01
The main phase of a geomagnetic storm develops differently from one storm to another. A description is given of the solar wind quantity which controls directly the development of the main phase of geomagnetic storms. The parameters involved include the solar wind speed, the magnetic field intensity, and the polar angle of the solar wind magnetic field projected onto the dawn-dusk plane. A redefinition of geomagnetic storm and auroral activity is given. It is pointed out that geomagnetic disturbances are caused by the magnetic fields of electric currents which are generated by the solar wind-magnetosphere dynamo. Attention is given to approaches for forecasting the occurrence and intensity of geomagnetic storms and ionospheric disturbances
Aviation Turbulence: Dynamics, Forecasting, and Response to Climate Change
Storer, Luke N.; Williams, Paul D.; Gill, Philip G.
2018-03-01
Atmospheric turbulence is a major hazard in the aviation industry and can cause injuries to passengers and crew. Understanding the physical and dynamical generation mechanisms of turbulence aids with the development of new forecasting algorithms and, therefore, reduces the impact that it has on the aviation industry. The scope of this paper is to review the dynamics of aviation turbulence, its response to climate change, and current forecasting methods at the cruising altitude of aircraft. Aviation-affecting turbulence comes from three main sources: vertical wind shear instabilities, convection, and mountain waves. Understanding these features helps researchers to develop better turbulence diagnostics. Recent research suggests that turbulence will increase in frequency and strength with climate change, and therefore, turbulence forecasting may become more important in the future. The current methods of forecasting are unable to predict every turbulence event, and research is ongoing to find the best solution to this problem by combining turbulence predictors and using ensemble forecasts to increase skill. The skill of operational turbulence forecasts has increased steadily over recent decades, mirroring improvements in our understanding. However, more work is needed—ideally in collaboration with the aviation industry—to improve observations and increase forecast skill, to help maintain and enhance aviation safety standards in the future.
Directory of Open Access Journals (Sweden)
Junhai Ma
2014-01-01
Full Text Available An important phenomenon in supply chain management which is known as the bullwhip effect suggests that demand variability increases as one moves up a supply chain. This paper contrasts the bullwhip effect for a two-stage supply chain consisting of one supplier and two retailers under three forecasting methods based on the market share. We can quantify the correlation coefficient between the two retailers clearly, in consideration of market share. The two retailers both employ the order-up-to inventory policy for replenishments. The bullwhip effect is measured, respectively, under the minimum mean squared error (MMSE, moving average (MA, and exponential smoothing (ES forecasting methods. The effect of autoregressive coefficient, lead time, and the market share on a bullwhip effect measure is investigated by using algebraic analysis and numerical simulation. And the comparison of the bullwhip effect under three forecasting methods is conducted. The conclusion suggests that different forecasting methods and various parameters lead to different bullwhip effects. Hence, the corresponding forecasting method should be chosen by the managers under different parameters in practice.
萩原, 由訓; 源栄, 正人; 三辻, 和弥; 野畑, 有秀; Yoshinori, HAGIWARA; Masato, MOTOSAKA; Kazuya, MITSUJI; Arihide, NOBATA; (株)大林組 技術研究所; 東北大学大学院工学研究科; 山形大学地域教育文化学部生活総合学科生活環境科学コース; (株)大林組 技術研究所; Obayashi Corporation Technical Research Institute; Graduate School of Eng., Tohoku University; Faculty of Education, Art and Science, Yamagata University
2011-01-01
The Japan Meteorological Agency(JMA) provides Earthquake Early Warnings(EEW) for advanced users from August 1, 2006. Advanced EEW users can forecaste seismic ground motion (example: Seismic Intensity, Peak Ground Acceleration) from information of the earthquake in EEW. But there are limits to the accuracy and the earliness of the forecasting. This paper describes regression equation to decrease the error and to increase rapidity of the forecast of ground motion parameters from Real Time Earth...
Directory of Open Access Journals (Sweden)
Fanping Zhang
2014-01-01
Full Text Available Streamflow forecasting has an important role in water resource management and reservoir operation. Support vector machine (SVM is an appropriate and suitable method for streamflow prediction due to its best versatility, robustness, and effectiveness. In this study, a wavelet transform particle swarm optimization support vector machine (WT-PSO-SVM model is proposed and applied for streamflow time series prediction. Firstly, the streamflow time series were decomposed into various details (Ds and an approximation (A3 at three resolution levels (21-22-23 using Daubechies (db3 discrete wavelet. Correlation coefficients between each D subtime series and original monthly streamflow time series are calculated. Ds components with high correlation coefficients (D3 are added to the approximation (A3 as the input values of the SVM model. Secondly, the PSO is employed to select the optimal parameters, C, ε, and σ, of the SVM model. Finally, the WT-PSO-SVM models are trained and tested by the monthly streamflow time series of Tangnaihai Station located in Yellow River upper stream from January 1956 to December 2008. The test results indicate that the WT-PSO-SVM approach provide a superior alternative to the single SVM model for forecasting monthly streamflow in situations without formulating models for internal structure of the watershed.
International Nuclear Information System (INIS)
Madoz-Escande, C.; Peyrus, J.-C.
1979-01-01
Hydrogeological studies are undertaken in the context of the radiological safety of nuclear plants to forecast consequences of accidental releases of radioactive pollutants into an aquifer (transfer time, concentration at points of emergence). This quantitative forecast is obtained with the aid of a mathematical model with sequential emission. This requires a knowledge of the physical parameters of the aquifer and of the behavior of the pollutant in relation to the water-bearing medium. The physical parameters of a saturated porous medium are presented with the aid of radioactive tracer tests on a model and also in the field. The initial results obtained in a sandy medium are presented. In view of the difficulty of extrapolating to field conditions the conclusions of tests on models, it was necessary to set up a mobile laboratory with which in situ studies could be undertaken. The behavior of the pollutant in relation to the water-bearing medium is the subject of preliminary laboratory research on the laws of adsorption under different pH and temperature conditions. The numerical results obtained call for confirmation in the field. A description is given of a method which should enable the distribution coefficients to be evaluated in situ
Directory of Open Access Journals (Sweden)
Hui Wan
2015-06-01
Full Text Available Sensitivity analysis is a fundamental approach to identify the most significant and sensitive parameters, helping us to understand complex hydrological models, particularly for time-consuming distributed flood forecasting models based on complicated theory with numerous parameters. Based on Sobol’ method, this study compared the sensitivity and interactions of distributed flood forecasting model parameters with and without accounting for correlation. Four objective functions: (1 Nash–Sutcliffe efficiency (ENS; (2 water balance coefficient (WB; (3 peak discharge efficiency (EP; and (4 time to peak efficiency (ETP were implemented to the Liuxihe model with hourly rainfall-runoff data collected in the Nanhua Creek catchment, Pearl River, China. Contrastive results for the sensitivity and interaction analysis were also illustrated among small, medium, and large flood magnitudes. Results demonstrated that the choice of objective functions had no effect on the sensitivity classification, while it had great influence on the sensitivity ranking for both uncorrelated and correlated cases. The Liuxihe model behaved and responded uniquely to various flood conditions. The results also indicated that the pairwise parameters interactions revealed a non-ignorable contribution to the model output variance. Parameters with high first or total order sensitivity indices presented a corresponding high second order sensitivity indices and correlation coefficients with other parameters. Without considering parameter correlations, the variance contributions of highly sensitive parameters might be underestimated and those of normally sensitive parameters might be overestimated. This research laid a basic foundation to improve the understanding of complex model behavior.
Runge-Kutta Integration of the Equal Width Wave Equation Using the Method of Lines
Directory of Open Access Journals (Sweden)
M. A. Banaja
2015-01-01
Full Text Available The equal width (EW equation governs nonlinear wave phenomena like waves in shallow water. Numerical solution of the (EW equation is obtained by using the method of lines (MOL based on Runge-Kutta integration. Using von Neumann stability analysis, the scheme is found to be unconditionally stable. Solitary wave motion and interaction of two solitary waves are studied using the proposed method. The three invariants of the motion are evaluated to determine the conservation properties of the generated scheme. Accuracy of the proposed method is discussed by computing the L2 and L∞ error norms. The results are found in good agreement with exact solution.
Experiments of Long-range Inspection Method in Straight Pipes using Ultrasonic Guided Waves
International Nuclear Information System (INIS)
Eom, H. S.; Lim, S. H.; Kim, J. H.; Joo, Y.S.
2006-02-01
This report describes experimental results of a long-range inspection method of pipes using ultrasonic guided waves. In chapter 2, theory of guided wave was reviewed. In chapter 3, equipment and procedures which were used in the experiments were described. Detailed specifications of the specimens described in chapter 4. In chapter 5, we analyzed characteristics of guided wave signals according to shapes and sizes of defects and presents results of various signal processing methods
Directory of Open Access Journals (Sweden)
Konstantinos Salpasaranis
2014-01-01
Full Text Available This paper proposes a modified Genetic Programming method for forecasting the mobile telecommunications subscribers’ population. The method constitutes an expansion of the hybrid Genetic Programming (hGP method improved by the introduction of diffusion models for technological forecasting purposes in the initial population, such as the Logistic, Gompertz, and Bass, as well as the Bi-Logistic and LogInLog. In addition, the aforementioned functions and models expand the function set of hGP. The application of the method in combination with macroeconomic indicators such as Gross Domestic Product per Capita (GDPpC and Consumer Prices Index (CPI leads to the creation of forecasting models and scenarios for medium- and long-term level of predictability. The forecasting module of the program has also been improved with the multi-levelled use of the statistical indices as fitness functions and model selection indices. The implementation of the modified-hGP in the datasets of mobile subscribers in the Organisation for Economic Cooperation and Development (OECD countries shows very satisfactory forecasting performance.
DEFF Research Database (Denmark)
Alvarez, Yuri; Cappellin, Cecilia; Las-Heras, Fernando
2008-01-01
A comparison between two recently developed methods for antenna diagnostics is presented. On one hand, the Spherical Wave Expansion-to-Plane Wave Expansion (SWE-PWE), based on the relationship between spherical and planar wave modes. On the other hand, the Sources Reconstruction Method (SRM), based...
The finite product method in the theory of linear wave propagation
DEFF Research Database (Denmark)
Sorokin, Sergey; Chapman, John
2012-01-01
of the method are presented for several non-trivial examples, that of symmetric/anti-symmetric elastic waves in a layer and in a thin plate. In each case, the method gives a sequence of polynomial approximations to the dispersion relation of remarkable accuracy over a broad range of frequencies and wave numbers...
A forecasting performance comparison of dynamic factor models based on static and dynamic methods
Directory of Open Access Journals (Sweden)
Marra Fabio Della
2017-03-01
Full Text Available We present a comparison of the forecasting performances of three Dynamic Factor Models on a large monthly data panel of macroeconomic and financial time series for the UE economy. The first model relies on static principal-component and was introduced by Stock and Watson (2002a, b. The second is based on generalized principal components and it was introduced by Forni, Hallin, Lippi and Reichlin (2000, 2005. The last model has been recently proposed by Forni, Hallin, Lippi and Zaffaroni (2015, 2016. The data panel is split into two parts: the calibration sample, from February 1986 to December 2000, is used to select the most performing specification for each class of models in a in- sample environment, and the proper sample, from January 2001 to November 2015, is used to compare the performances of the selected models in an out-of-sample environment. The metholodogical approach is analogous to Forni, Giovannelli, Lippi and Soccorsi (2016, but also the size of the rolling window is empirically estimated in the calibration process to achieve more robustness. We find that, on the proper sample, the last model is the most performing for the Inflation. However, mixed evidencies appear over the proper sample for the Industrial Production.
Forecasting of palm oil price in Malaysia using linear and nonlinear methods
Nor, Abu Hassan Shaari Md; Sarmidi, Tamat; Hosseinidoust, Ehsan
2014-09-01
The first question that comes to the mind is: "How can we predict the palm oil price accurately?" This question is the authorities, policy makers and economist's question for a long period of time. The first reason is that in the recent years Malaysia showed a comparative advantage in palm oil production and has become top producer and exporter in the world. Secondly, palm oil price plays significant role in government budget and represents important source of income for Malaysia, which potentially can influence the magnitude of monetary policies and eventually have an impact on inflation. Thirdly, knowledge on the future trends would be helpful in the planning and decision making procedures and will generate precise fiscal and monetary policy. Daily data on palm oil prices along with the ARIMA models, neural networks and fuzzy logic systems are employed in this paper. Empirical findings indicate that the dynamic neural network of NARX and the hybrid system of ANFIS provide higher accuracy than the ARIMA and static neural network for forecasting the palm oil price in Malaysia.
International Nuclear Information System (INIS)
Mak, H.
1995-01-01
Slides used in a presentation at The Power of Change Conference in Vancouver, BC in April 1995 about the changing needs for load forecasting were presented. Technological innovations and population increase were said to be the prime driving forces behind the changing needs in load forecasting. Structural changes, market place changes, electricity supply planning changes, and changes in planning objectives were other factors discussed. It was concluded that load forecasting was a form of information gathering, that provided important market intelligence
Yin, Yip Chee; Hock-Eam, Lim
2012-09-01
Our empirical results show that we can predict GDP growth rate more accurately in continent with fewer large economies, compared to smaller economies like Malaysia. This difficulty is very likely positively correlated with subsidy or social security policies. The stage of economic development and level of competiveness also appears to have interactive effects on this forecast stability. These results are generally independent of the forecasting procedures. Countries with high stability in their economic growth, forecasting by model selection is better than model averaging. Overall forecast weight averaging (FWA) is a better forecasting procedure in most countries. FWA also outperforms simple model averaging (SMA) and has the same forecasting ability as Bayesian model averaging (BMA) in almost all countries.
Producing accurate wave propagation time histories using the global matrix method
International Nuclear Information System (INIS)
Obenchain, Matthew B; Cesnik, Carlos E S
2013-01-01
This paper presents a reliable method for producing accurate displacement time histories for wave propagation in laminated plates using the global matrix method. The existence of inward and outward propagating waves in the general solution is highlighted while examining the axisymmetric case of a circular actuator on an aluminum plate. Problems with previous attempts to isolate the outward wave for anisotropic laminates are shown. The updated method develops a correction signal that can be added to the original time history solution to cancel the inward wave and leave only the outward propagating wave. The paper demonstrates the effectiveness of the new method for circular and square actuators bonded to the surface of isotropic laminates, and these results are compared with exact solutions. Results for circular actuators on cross-ply laminates are also presented and compared with experimental results, showing the ability of the new method to successfully capture the displacement time histories for composite laminates. (paper)
Numerical investigation of freak waves
Chalikov, D.
2009-04-01
Paper describes the results of more than 4,000 long-term (up to thousands of peak-wave periods) numerical simulations of nonlinear gravity surface waves performed for investigation of properties and estimation of statistics of extreme (‘freak') waves. The method of solution of 2-D potential wave's equations based on conformal mapping is applied to the simulation of wave behavior assigned by different initial conditions, defined by JONSWAP and Pierson-Moskowitz spectra. It is shown that nonlinear wave evolution sometimes results in appearance of very big waves. The shape of freak waves varies within a wide range: some of them are sharp-crested, others are asymmetric, with a strong forward inclination. Some of them can be very big, but not steep enough to create dangerous conditions for vessels (but not for fixed objects). Initial generation of extreme waves can occur merely as a result of group effects, but in some cases the largest wave suddenly starts to grow. The growth is followed sometimes by strong concentration of wave energy around a peak vertical. It is taking place in the course of a few peak wave periods. The process starts with an individual wave in a physical space without significant exchange of energy with surrounding waves. Sometimes, a crest-to-trough wave height can be as large as nearly three significant wave heights. On the average, only one third of all freak waves come to breaking, creating extreme conditions, however, if a wave height approaches the value of three significant wave heights, all of the freak waves break. The most surprising result was discovery that probability of non-dimensional freak waves (normalized by significant wave height) is actually independent of density of wave energy. It does not mean that statistics of extreme waves does not depend on wave energy. It just proves that normalization of wave heights by significant wave height is so effective, that statistics of non-dimensional extreme waves tends to be independent
Capacitive acoustic wave detector and method of using same
Yost, William T. (Inventor)
1994-01-01
A capacitor having two substantially parallel conductive faces is acoustically coupled to a conductive sample end such that the sample face is one end of the capacitor. A non-contacting dielectric may serve as a spacer between the two conductive plates. The formed capacitor is connected to an LC oscillator circuit such as a Hartley oscillator circuit producing an output frequency which is a function of the capacitor spacing. This capacitance oscillates as the sample end coating is oscillated by an acoustic wave generated in the sample by a transmitting transducer. The electrical output can serve as an absolute indicator of acoustic wave displacement.
太阳能预报方法及其应用和问题%A Review on Methods of Solar Energy Forecasting and Its Application
Institute of Scientific and Technical Information of China (English)
马金玉; 罗勇; 申彦波; 李世奎
2011-01-01
太阳能预报包括预测太阳辐射量和光伏发电功率,对光伏发电系统并网运行有重要意义,是当前太阳能开发利用的一个关键问题.本文对国内外太阳能预报方法进行了扼要的评述,归纳了太阳能预报的机理及其方法在光伏发电中的应用.太阳辐射的预报方法主要有传统统计、神经网络、卫星遥感和数值模拟等方法.文中基于光伏发电应用的需求,分析了不同预报方法的优点和不足,并探讨了若干有待进一步改善的问题,展望了国内太阳能预报技术方法的发展和应用前景.%Solar forecasting, consisting of solar radiation forecasting and photovoltaic solar power forecasting, is important for photovoltaic power generation systems in network operation. In recent years, with the development of the solar industry, the demand for solar energy forecasting is increasing. Solar energy prediction methods have been developed in developed country. Our solar photovoltaic technology research is, however, at a primary stage, with only a few universities and institutes conducting simulation-based research, little of which accounts for meteorological factors.According to predicted solar physical factors, the prediction can be generally divided into two categories. One is to predict solar radiation which requires the calculation of photovoltaic power according to the output photoelectric conversion efficiency. The other is direct prediction of output power of PV systems. As the domestic forecast on solar energy technologies and applications are rarely reported, mechanisms of solar forecasting, methods and applications in photovoltaic power generation were reviewed based on the demand for photovoltaic applications. This review would provide an important basis for domestic solar photovoltaic power generation development. This paper focuses on the situations of solar energy prediction at home and abroad, and summarizes the principles of solar energy
Directory of Open Access Journals (Sweden)
H. O. Bakodah
2013-01-01
Full Text Available A method of lines approach to the numerical solution of nonlinear wave equations typified by the regularized long wave (RLW is presented. The method developed uses a finite differences discretization to the space. Solution of the resulting system was obtained by applying fourth Runge-Kutta time discretization method. Using Von Neumann stability analysis, it is shown that the proposed method is marginally stable. To test the accuracy of the method some numerical experiments on test problems are presented. Test problems including solitary wave motion, two-solitary wave interaction, and the temporal evaluation of a Maxwellian initial pulse are studied. The accuracy of the present method is tested with and error norms and the conservation properties of mass, energy, and momentum under the RLW equation.
Localized atomic basis set in the projector augmented wave method
DEFF Research Database (Denmark)
Larsen, Ask Hjorth; Vanin, Marco; Mortensen, Jens Jørgen
2009-01-01
We present an implementation of localized atomic-orbital basis sets in the projector augmented wave (PAW) formalism within the density-functional theory. The implementation in the real-space GPAW code provides a complementary basis set to the accurate but computationally more demanding grid...
Cell detachment method using shock wave induced cavitation
Junge, L.; Junge, L.; Ohl, C.D.; Wolfrum, B.; Arora, M.; Ikink, R.
2003-01-01
The detachment of adherent HeLa cells from a substrate after the interaction with a shock wave is analyzed. Cavitation bubbles are formed in the trailing, negative pressure cycle following the shock front. We find that the regions of cell detachment are strongly correlated with spatial presence of
Ashraf, M. A. M.; Kumar, N. S.; Yusoh, R.; Hazreek, Z. A. M.; Aziman, M.
2018-04-01
Site classification utilizing average shear wave velocity (Vs(30) up to 30 meters depth is a typical parameter. Numerous geophysical methods have been proposed for estimation of shear wave velocity by utilizing assortment of testing configuration, processing method, and inversion algorithm. Multichannel Analysis of Surface Wave (MASW) method is been rehearsed by numerous specialist and professional to geotechnical engineering for local site characterization and classification. This study aims to determine the site classification on soft and hard ground using MASW method. The subsurface classification was made utilizing National Earthquake Hazards Reduction Program (NERHP) and international Building Code (IBC) classification. Two sites are chosen to acquire the shear wave velocity which is in the state of Pulau Pinang for soft soil and Perlis for hard rock. Results recommend that MASW technique can be utilized to spatially calculate the distribution of shear wave velocity (Vs(30)) in soil and rock to characterize areas.
Analysis of efficient preconditioned defect correction methods for nonlinear water waves
DEFF Research Database (Denmark)
Engsig-Karup, Allan Peter
2014-01-01
Robust computational procedures for the solution of non-hydrostatic, free surface, irrotational and inviscid free-surface water waves in three space dimensions can be based on iterative preconditioned defect correction (PDC) methods. Such methods can be made efficient and scalable to enable...... prediction of free-surface wave transformation and accurate wave kinematics in both deep and shallow waters in large marine areas or for predicting the outcome of experiments in large numerical wave tanks. We revisit the classical governing equations are fully nonlinear and dispersive potential flow...... equations. We present new detailed fundamental analysis using finite-amplitude wave solutions for iterative solvers. We demonstrate that the PDC method in combination with a high-order discretization method enables efficient and scalable solution of the linear system of equations arising in potential flow...
Hsieh, Yi-Kai; Omura, Yoshiharu
2017-10-01
We investigate the properties of whistler mode wave-particle interactions at oblique wave normal angles to the background magnetic field. We find that electromagnetic energy of waves at frequencies below half the electron cyclotron frequency can flow nearly parallel to the ambient magnetic field. We thereby confirm that the gyroaveraging method, which averages the cyclotron motion to the gyrocenter and reduces the simulation from two-dimensional to one-dimensional, is valid for oblique wave-particle interaction. Multiple resonances appear for oblique propagation but not for parallel propagation. We calculate the possible range of resonances with the first-order resonance condition as a function of electron kinetic energy and equatorial pitch angle. To reveal the physical process and the efficiency of electron acceleration by multiple resonances, we assume a simple uniform wave model with constant amplitude and frequency in space and time. We perform test particle simulations with electrons starting at specific equatorial pitch angles and kinetic energies. The simulation results show that multiple resonances contribute to acceleration and pitch angle scattering of energetic electrons. Especially, we find that electrons with energies of a few hundred keV can be accelerated efficiently to a few MeV through the n = 0 Landau resonance.
Approximated calculation of the vacuum wave function and vacuum energy of the LGT with RPA method
International Nuclear Information System (INIS)
Hui Ping
2004-01-01
The coupled cluster method is improved with the random phase approximation (RPA) to calculate vacuum wave function and vacuum energy of 2 + 1 - D SU(2) lattice gauge theory. In this calculating, the trial wave function composes of single-hollow graphs. The calculated results of vacuum wave functions show very good scaling behaviors at weak coupling region l/g 2 >1.2 from the third order to the sixth order, and the vacuum energy obtained with RPA method is lower than the vacuum energy obtained without RPA method, which means that this method is a more efficient one
Analysis of a plane stress wave by the moving least squares method
Directory of Open Access Journals (Sweden)
Wojciech Dornowski
2014-08-01
Full Text Available A meshless method based on the moving least squares approximation is applied to stress wave propagation analysis. Two kinds of node meshes, the randomly generated mesh and the regular mesh are used. The nearest neighbours’ problem is developed from a triangulation that satisfies minimum edges length conditions. It is found that this method of neighbours’ choice significantly improves the solution accuracy. The reflection of stress waves from the free edge is modelled using fictitious nodes (outside the plate. The comparison with the finite difference results also demonstrated the accuracy of the proposed approach.[b]Keywords[/b]: civil engineering, meshless method, moving least squares method, elastic waves
Directory of Open Access Journals (Sweden)
Zhong-ye Tian
2014-01-01
Full Text Available The seismic responses of a long-span cable-stayed bridge under uniform excitation and traveling wave excitation in the longitudinal direction are, respectively, computed. The numerical results show that the bridge’s peak seismic responses vary significantly as the apparent wave velocity decreases. Therefore, the traveling wave effect must be considered in the seismic design of long-span bridges. The bridge’s peak seismic responses do not vary monotonously with the apparent wave velocity due to the traveling wave resonance. A new traveling wave excitation method that can simplify the multisupport excitation process into a two-support excitation process is developed.
DEFF Research Database (Denmark)
Ibsen, Lars Bo
2008-01-01
Estimates for the amount of potential wave energy in the world range from 1-10 TW. The World Energy Council estimates that a potential 2TW of energy is available from the world’s oceans, which is the equivalent of twice the world’s electricity production. Whilst the recoverable resource is many...... times smaller it remains very high. For example, whilst there is enough potential wave power off the UK to supply the electricity demands several times over, the economically recoverable resource for the UK is estimated at 25% of current demand; a lot less, but a very substantial amount nonetheless....
Directory of Open Access Journals (Sweden)
Polshkov Yulian M.
2013-11-01
Full Text Available The article considers data on the gross domestic product, consumer expenditures, gross investments and volume of foreign trade for the national economy. It is assumed that time is a discrete variable with one year iteration. The article uses finite-difference equations. It considers models with a high degree of the regulatory function of the state with respect to the consumer market. The econometric component is based on the hypothesis that each of the above said macro-economic indicators for this year depends on the gross domestic product for the previous time periods. Such an assumption gives a possibility to engage the least-squares method for building up linear models of the pair regression. The article obtains the time series model, which allows building point and interval forecasts for the gross domestic product for the next year based on the values of the gross domestic product for the current and previous years. The article draws a conclusion that such forecasts could be considered justified at least in the short-term prospect. From the mathematical point of view the built model is a heterogeneous finite-difference equation of the second order with constant ratios. The article describes specific features of such equations. It illustrates graphically the analytical view of solutions of the finite-difference equation. This gives grounds to differentiate national economies as sustainable growth economies, one-sided, weak or being in the stage of successful re-formation. The article conducts comparison of the listed types with specific economies of modern states.
Failed fuel rod detection method by ultrasonic wave
International Nuclear Information System (INIS)
Takamatsu, Masatoshi; Muraoka, Shoichi; Ono, Yukio; Yasojima, Yujiro.
1990-01-01
Ultrasonic wave signals sent from an ultrasonic receiving element are supplied to an evaluation circuit by way of a gate. A table for gate opening and closing timings at the detecting position in each of the fuel rods in a fuel assembly is stored in a memory. A fuel rod is placed between an ultrasonic transmitting element and the receiving element to determine the positions of the transmitting element and the receiving element by positional sensors. The opening and closing timings at the positions corresponding to the result of the detection are read out from the table, and the gates are opened and closed by the timing. This can introduce the ultrasonic wave signals transmitted through a control rod always to the evaluation circuit passing through the gate. Accordingly, the state of failure of the fuel rod can be detected accurately. (I.N.)
Singular value decomposition methods for wave propagation analysis
Czech Academy of Sciences Publication Activity Database
Santolík, Ondřej; Parrot, M.; Lefeuvre, F.
2003-01-01
Roč. 38, č. 1 (2003), s. 10-1-10-13 ISSN 0048-6604 R&D Projects: GA ČR GA205/01/1064 Grant - others:Barrande(CZ) 98039/98055 Institutional research plan: CEZ:AV0Z3042911; CEZ:MSM 113200004 Keywords : wave propagation * singular value decomposition Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 0.832, year: 2003
Perfectly Matched Layer for the Wave Equation Finite Difference Time Domain Method
Miyazaki, Yutaka; Tsuchiya, Takao
2012-07-01
The perfectly matched layer (PML) is introduced into the wave equation finite difference time domain (WE-FDTD) method. The WE-FDTD method is a finite difference method in which the wave equation is directly discretized on the basis of the central differences. The required memory of the WE-FDTD method is less than that of the standard FDTD method because no particle velocity is stored in the memory. In this study, the WE-FDTD method is first combined with the standard FDTD method. Then, Berenger's PML is combined with the WE-FDTD method. Some numerical demonstrations are given for the two- and three-dimensional sound fields.
Element-by-element parallel spectral-element methods for 3-D teleseismic wave modeling
Liu, Shaolin
2017-09-28
The development of an efficient algorithm for teleseismic wave field modeling is valuable for calculating the gradients of the misfit function (termed misfit gradients) or Fréchet derivatives when the teleseismic waveform is used for adjoint tomography. Here, we introduce an element-by-element parallel spectral-element method (EBE-SEM) for the efficient modeling of teleseismic wave field propagation in a reduced geology model. Under the plane-wave assumption, the frequency-wavenumber (FK) technique is implemented to compute the boundary wave field used to construct the boundary condition of the teleseismic wave incidence. To reduce the memory required for the storage of the boundary wave field for the incidence boundary condition, a strategy is introduced to efficiently store the boundary wave field on the model boundary. The perfectly matched layers absorbing boundary condition (PML ABC) is formulated using the EBE-SEM to absorb the scattered wave field from the model interior. The misfit gradient can easily be constructed in each time step during the calculation of the adjoint wave field. Three synthetic examples demonstrate the validity of the EBE-SEM for use in teleseismic wave field modeling and the misfit gradient calculation.
Characterization of non-Q wave infarction by radioisotopic methods
International Nuclear Information System (INIS)
Parodi, O.; Marzullo, P.; Marcassa, C.; Sambuceti, G.
1986-01-01
The syndrome of ''subendocardial infarction'' or ''non Q-wave infarction'', which has been defined only in terms of the presence of (ECG) electrocardiographic evidence of necrosis, has been shown to be poorly corelated with anatomical and pathomorphological findings. In a large number of patients who had been diagnosed as having this commonly described entity, the usefulness of a multiparametric approach was evaluated. The proper assessment of such patients may necessitate flow studies with T1-201, which is, however, a marker with known limitations: labelled microspheres; or Rb-82, a generator-produced positron emitter. Metabolic studies using fluoro-F-18-deoxyglucose or C-11 palmitate will defect impairment of fatty acid oxidation and residual glucose metabolism; wall motion studies will demonstrate impairment to a variable extent. The presence or absence of Q-waves does not distinguish between transmural and subendocardial infarction. The size and location of ST-T wave changes does not indicate the site of infarction. Patients with this syndrome exhibit a wide spectrum of wal motion abnormalities and usually have diffuse coronary lesions. Since conventional clinical investigations cannot be used to predict the presence and extent of necrosis, or its exact location, the studies performed should be directed toward the appropriate evaluation of perfusion and metabolic patterns. This emphasizes the important point that clinicians may investigate this syndrome by the use of the proper approach. (orig.)
An efficient wave extrapolation method for anisotropic media with tilt
Waheed, Umair bin
2015-03-23
Wavefield extrapolation operators for elliptically anisotropic media offer significant cost reduction compared with that for the transversely isotropic case, particularly when the axis of symmetry exhibits tilt (from the vertical). However, elliptical anisotropy does not provide accurate wavefield representation or imaging for transversely isotropic media. Therefore, we propose effective elliptically anisotropic models that correctly capture the kinematic behaviour of wavefields for transversely isotropic media. Specifically, we compute source-dependent effective velocities for the elliptic medium using kinematic high-frequency representation of the transversely isotropic wavefield. The effective model allows us to use cheaper elliptic wave extrapolation operators. Despite the fact that the effective models are obtained by matching kinematics using high-frequency asymptotic, the resulting wavefield contains most of the critical wavefield components, including frequency dependency and caustics, if present, with reasonable accuracy. The methodology developed here offers a much better cost versus accuracy trade-off for wavefield computations in transversely isotropic media, particularly for media of low to moderate complexity. In addition, the wavefield solution is free from shear-wave artefacts as opposed to the conventional finite-difference-based transversely isotropic wave extrapolation scheme. We demonstrate these assertions through numerical tests on synthetic tilted transversely isotropic models.
An efficient wave extrapolation method for anisotropic media with tilt
Waheed, Umair bin; Alkhalifah, Tariq Ali
2015-01-01
Wavefield extrapolation operators for elliptically anisotropic media offer significant cost reduction compared with that for the transversely isotropic case, particularly when the axis of symmetry exhibits tilt (from the vertical). However, elliptical anisotropy does not provide accurate wavefield representation or imaging for transversely isotropic media. Therefore, we propose effective elliptically anisotropic models that correctly capture the kinematic behaviour of wavefields for transversely isotropic media. Specifically, we compute source-dependent effective velocities for the elliptic medium using kinematic high-frequency representation of the transversely isotropic wavefield. The effective model allows us to use cheaper elliptic wave extrapolation operators. Despite the fact that the effective models are obtained by matching kinematics using high-frequency asymptotic, the resulting wavefield contains most of the critical wavefield components, including frequency dependency and caustics, if present, with reasonable accuracy. The methodology developed here offers a much better cost versus accuracy trade-off for wavefield computations in transversely isotropic media, particularly for media of low to moderate complexity. In addition, the wavefield solution is free from shear-wave artefacts as opposed to the conventional finite-difference-based transversely isotropic wave extrapolation scheme. We demonstrate these assertions through numerical tests on synthetic tilted transversely isotropic models.
A Method and an Apparatus for Generating a Phase-Modulated Wave Front of Electromagnetic Radiation
DEFF Research Database (Denmark)
2002-01-01
The present invention provides a method and a system for generating a phase-modulated wave front. According to the present invention, the spatial phase-modulation is not performed on the different parts of the wave front individually as in known POSLMs. Rather, the spatial phase-modulation of the...
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
Using direct algebraic method,exact solitary wave solutions are performed for a class of third order nonlinear dispersive disipative partial differential equations. These solutions are obtained under certain conditions for the relationship between the coefficients of the equation. The exact solitary waves of this class are rational functions of real exponentials of kink-type solutions.
The two-wave X-ray field calculated by means of integral-equation methods
International Nuclear Information System (INIS)
Bremer, J.
1984-01-01
The problem of calculating the two-wave X-ray field on the basis of the Takagi-Taupin equations is discussed for the general case of curved lattice planes. A two-dimensional integral equation which incorporates the nature of the incoming radiation, the form of the crystal/vacuum boundary, and the curvature of the structure, is deduced. Analytical solutions for the symmetrical Laue case with incoming plane waves are obtained directly for perfect crystals by means of iteration. The same method permits a simple derivation of the narrow-wave Laue and Bragg cases. Modulated wave fronts are discussed, and it is shown that a cut-off in the width of an incoming plane wave leads to lateral oscillations which are superimposed on the Pendelloesung fringes. Bragg and Laue shadow fields are obtained. The influence of a non-zero kernel is discussed and a numerical procedure for calculating wave amplitudes in curved crystals is presented. (Auth.)
Kucharavy , Dmitry; De Guio , Roland
2005-01-01
International audience; The ability to foresee future technology is a key task of Innovative Design. The paper focuses on the obstacles to reliable prediction of technological evolution for the purpose of Innovative Design. First, a brief analysis of problems for existing forecasting methods is presented. The causes for the complexity of technology prediction are discussed in the context of reduction of the forecast errors. Second, using a contradiction analysis, a set of problems related to ...
A coupled DEM-CFD method for impulse wave modelling
Zhao, Tao; Utili, Stefano; Crosta, GiovanBattista
2015-04-01
Rockslides can be characterized by a rapid evolution, up to a possible transition into a rock avalanche, which can be associated with an almost instantaneous collapse and spreading. Different examples are available in the literature, but the Vajont rockslide is quite unique for its morphological and geological characteristics, as well as for the type of evolution and the availability of long term monitoring data. This study advocates the use of a DEM-CFD framework for the modelling of the generation of hydrodynamic waves due to the impact of a rapid moving rockslide or rock-debris avalanche. 3D DEM analyses in plane strain by a coupled DEM-CFD code were performed to simulate the rockslide from its onset to the impact with still water and the subsequent wave generation (Zhao et al., 2014). The physical response predicted is in broad agreement with the available observations. The numerical results are compared to those published in the literature and especially to Crosta et al. (2014). According to our results, the maximum computed run up amounts to ca. 120 m and 170 m for the eastern and western lobe cross sections, respectively. These values are reasonably similar to those recorded during the event (i.e. ca. 130 m and 190 m respectively). In these simulations, the slope mass is considered permeable, such that the toe region of the slope can move submerged in the reservoir and the impulse water wave can also flow back into the slope mass. However, the upscaling of the grains size in the DEM model leads to an unrealistically high hydraulic conductivity of the model, such that only a small amount of water is splashed onto the northern bank of the Vajont valley. The use of high fluid viscosity and coarse grain model has shown the possibility to model more realistically both the slope and wave motions. However, more detailed slope and fluid properties, and the need for computational efficiency should be considered in future research work. This aspect has also been
The Method for Assessing and Forecasting Value of Knowledge in SMEs – Research Results
Directory of Open Access Journals (Sweden)
Justyna Patalas-Maliszewska
2010-10-01
Full Text Available Decisions by SMEs regarding knowledge development are made at a strategic level (Haas-Edersheim, 2007. Related to knowledge management are approaches to "measure" knowledge, where literature distinguishes between qualitative and quantitative methods of valuating intellectual capital. Although there is a quite range of such methods to build an intellectual capital reporting system, none of them is really widely recognized. This work presents a method enabling assessing the effectiveness of investing in human resources, taking into consideration existing methods. The method presented is focusing on SMEs (taking into consideration their importance for, especially, regional development. It consists of four parts: an SME reference model, an indicator matrix to assess investments into knowledge, innovation indicators, and the GMDH algorithm for decision making. The method presented is exemplified by a case study including 10 companies.
Directory of Open Access Journals (Sweden)
Oleksandr Yu. Melnykov
2018-02-01
Full Text Available The existing forms and methods of assessing the work of teachers of higher educational institutions are described. The conclusion is made that the combination of indicators into groups (categories and the introduction of different weight factors depends on the specifics of the institution and the prevailing ideas about the priority of this or that type of activity. Practically all the considered methods do not take into account the change in the contribution share of each teacher in the integral indicator of the work of the whole department (department, faculty. The goal was to predict the change in the contribution of an individual teacher to the indicators of a higher education institution by means of mathematical modeling and intellectual decision-making. The prediction task is identified as a suitable data mining task. Methods for forecasting the assessment of the work of teachers - regression and neural network - were chosen. An object-oriented model of a projected computer system in the language of visual modeling of UML is described. Diagrams of use cases, classes and states are given. The program implementation of the intellectual decision-making system for evaluating the work of teachers of a higher education institution and an example of the system's operation based on real data are described. Conclusions are made about a possible change in the contribution share of each teacher in the indicators of the department.
Directory of Open Access Journals (Sweden)
Xiao-zhe Bai
2017-01-01
Full Text Available Globally, cyanobacteria blooms frequently occur, and effective prediction of cyanobacteria blooms in lakes and reservoirs could constitute an essential proactive strategy for water-resource protection. However, cyanobacteria blooms are very complicated because of the internal stochastic nature of the system evolution and the external uncertainty of the observation data. In this study, an adaptive-clustering algorithm is introduced to obtain some typical operating intervals. In addition, the number of nearest neighbors used for modeling was optimized by particle swarm optimization. Finally, a fuzzy linear regression method based on error-correction was used to revise the model dynamically near the operating point. We found that the combined method can characterize the evolutionary track of cyanobacteria blooms in lakes and reservoirs. The model constructed in this paper is compared to other cyanobacteria-bloom forecasting methods (e.g., phase space reconstruction and traditional-clustering linear regression, and, then, the average relative error and average absolute error are used to compare the accuracies of these models. The results suggest that the proposed model is superior. As such, the newly developed approach achieves more precise predictions, which can be used to prevent the further deterioration of the water environment.
Quantile forecast discrimination ability and value
DEFF Research Database (Denmark)
Ben Bouallègue, Zied; Pinson, Pierre; Friederichs, Petra
2015-01-01
While probabilistic forecast verification for categorical forecasts is well established, some of the existing concepts and methods have not found their equivalent for the case of continuous variables. New tools dedicated to the assessment of forecast discrimination ability and forecast value are ...... is illustrated based on synthetic datasets, as well as for the case of global radiation forecasts from the high resolution ensemble COSMO-DE-EPS of the German Weather Service....
Directory of Open Access Journals (Sweden)
J. Cho
2016-10-01
Full Text Available The APEC Climate Center (APCC produces climate prediction information utilizing a multi-climate model ensemble (MME technique. In this study, four different downscaling methods, in accordance with the degree of utilizing the seasonal climate prediction information, were developed in order to improve predictability and to refine the spatial scale. These methods include: (1 the Simple Bias Correction (SBC method, which directly uses APCC's dynamic prediction data with a 3 to 6 month lead time; (2 the Moving Window Regression (MWR method, which indirectly utilizes dynamic prediction data; (3 the Climate Index Regression (CIR method, which predominantly uses observation-based climate indices; and (4 the Integrated Time Regression (ITR method, which uses predictors selected from both CIR and MWR. Then, a sampling-based temporal downscaling was conducted using the Mahalanobis distance method in order to create daily weather inputs to the Soil and Water Assessment Tool (SWAT model. Long-term predictability of water quality within the Wecheon watershed of the Nakdong River Basin was evaluated. According to the Korean Ministry of Environment's Provisions of Water Quality Prediction and Response Measures, modeling-based predictability was evaluated by using 3-month lead prediction data issued in February, May, August, and November as model input of SWAT. Finally, an integrated approach, which takes into account various climate information and downscaling methods for water quality prediction, was presented. This integrated approach can be used to prevent potential problems caused by extreme climate in advance.
A New Alternative in Urban Geophysics: Multi-Channel Analysis of Surface Waves (MASW) Method
International Nuclear Information System (INIS)
Ozcep, F.
2007-01-01
Geophysical studies are increasingly being applied to geotechnical investigations as they can identify soil properties and soil boundaries. Other advantage is that many of these methods are non-invasive and environment friendly. Soil stiffness is one of the critical material parameters considered during an early stage of most foundation construction. It is related directly to the stability of structural load, especially as it relates to possible earthquake hazard. Soil lacking sufficient stiffness for a given load can experience a significant reduction in strength under earthquake shaking resulting in liquefaction, a condition responsible for tremendous amounts of damage from earthquakes around the world The multichannel analysis of surface waves (MASW) method originated from the traditional seismic exploration approach that employs multiple (twelve or more) receivers placed along a linear survey line. Main advantage is its capability of recognizing different types of seismic waves based on wave propagation characteristics such as velocity and attenuation. The MASW method utilizes this capability to discriminate the fundamental-mode Rayleigh wave against all other types of surface and body waves generated not only from the active seismic source but also from the ambient site conditions. Dispersive characteristics of seismic waves are imaged from an objective 2-D wave field transformation. The present paper indicates results from MASW survey at different urban site in Turkey. MASW techniques will prove to be important tools for obtaining shear wave velocity and evaluating liquefaction potential, soil bearing capacity and soil amplification, etc. for future geophysical and geotechnical engineering community
International Nuclear Information System (INIS)
Ostachowicz, W; Kudela, P
2010-01-01
A Spectral Element Method is used for wave propagation modelling. A 3D solid spectral element is derived with shape functions based on Lagrange interpolation and Gauss-Lobatto-Legendre points. This approach is applied for displacement approximation suited for fundamental modes of Lamb waves as well as potential distribution in piezoelectric transducers. The novelty is the model geometry extension from flat to curved elements for application in shell-like structures. Exemplary visualisations of waves excited by the piezoelectric transducers in curved shell structure made of aluminium alloy are presented. Simple signal analysis of wave interaction with crack is performed. The crack is modelled by separation of appropriate nodes between elements. An investigation of influence of the crack length on wave propagation signals is performed. Additionally, some aspects of the spectral element method implementation are discussed.
Numerical simulation of electromagnetic wave propagation using time domain meshless method
International Nuclear Information System (INIS)
Ikuno, Soichiro; Fujita, Yoshihisa; Itoh, Taku; Nakata, Susumu; Nakamura, Hiroaki; Kamitani, Atsushi
2012-01-01
The electromagnetic wave propagation in various shaped wave guide is simulated by using meshless time domain method (MTDM). Generally, Finite Differential Time Domain (FDTD) method is applied for electromagnetic wave propagation simulation. However, the numerical domain should be divided into rectangle meshes if FDTD method is applied for the simulation. On the other hand, the node disposition of MTDM can easily describe the structure of arbitrary shaped wave guide. This is the large advantage of the meshless time domain method. The results of computations show that the damping rate is stably calculated in case with R < 0.03, where R denotes a support radius of the weight function for the shape function. And the results indicate that the support radius R of the weight functions should be selected small, and monomials must be used for calculating the shape functions. (author)
Traveling Wave Solutions of ZK-BBM Equation Sine-Cosine Method
Directory of Open Access Journals (Sweden)
Sadaf Bibi
2014-03-01
Full Text Available Travelling wave solutions are obtained by using a relatively new technique which is called sine-cosine method for ZK-BBM equations. Solution procedure and obtained results re-confirm the efficiency of the proposed scheme.
Directional spectrum of ocean waves from array measurements using phase/time/path difference methods
Digital Repository Service at National Institute of Oceanography (India)
Fernandes, A.A.; Sarma, Y.V.B.; Menon, H.B.
Wave direction has for the first time been consistently, accurately and unambiguously evaluated from array measurements using the phase/time/path difference (PTPD) methods of Esteva in case of polygonal arrays and Borgman in case of linear arrays...
Wave resistance calculation method combining Green functions based on Rankine and Kelvin source
Directory of Open Access Journals (Sweden)
LI Jingyu
2017-12-01
Full Text Available [Ojectives] At present, the Boundary Element Method(BEM of wave-making resistance mostly uses a model in which the velocity distribution near the hull is solved first, and the pressure integral is then calculated using the Bernoulli equation. However,the process of this model of wave-making resistance is complex and has low accuracy.[Methods] To address this problem, the present paper deduces a compound method for the quick calculation of ship wave resistance using the Rankine source Green function to solve the hull surface's source density, and combining the Lagally theorem concerning source point force calculation based on the Kelvin source Green function so as to solve the wave resistance. A case for the Wigley model is given.[Results] The results show that in contrast to the thin ship method of the linear wave resistance theorem, this method has higher precision, and in contrast to the method which completely uses the Kelvin source Green function, this method has better computational efficiency.[Conclusions] In general, the algorithm in this paper provides a compromise between precision and efficiency in wave-making resistance calculation.
Yanagihara, Kota; Kubo, Shin; Dodin, Ilya; Nakamura, Hiroaki; Tsujimura, Toru
2017-10-01
Geometrical Optics Ray-tracing is a reasonable numerical analytic approach for describing the Electron Cyclotron resonance Wave (ECW) in slowly varying spatially inhomogeneous plasma. It is well known that the result with this conventional method is adequate in most cases. However, in the case of Helical fusion plasma which has complicated magnetic structure, strong magnetic shear with a large scale length of density can cause a mode coupling of waves outside the last closed flux surface, and complicated absorption structure requires a strong focused wave for ECH. Since conventional Ray Equations to describe ECW do not have any terms to describe the diffraction, polarization and wave decay effects, we can not describe accurately a mode coupling of waves, strong focus waves, behavior of waves in inhomogeneous absorption region and so on. For fundamental solution of these problems, we consider the extension of the Ray-tracing method. Specific process is planned as follows. First, calculate the reference ray by conventional method, and define the local ray-base coordinate system along the reference ray. Then, calculate the evolution of the distributions of amplitude and phase on ray-base coordinate step by step. The progress of our extended method will be presented.
Topology optimization of bounded acoustic problems using the hybrid finite element-wave based method
DEFF Research Database (Denmark)
Goo, Seongyeol; Wang, Semyung; Kook, Junghwan
2017-01-01
This paper presents an alternative topology optimization method for bounded acoustic problems that uses the hybrid finite element-wave based method (FE-WBM). The conventional method for the topology optimization of bounded acoustic problems is based on the finite element method (FEM), which...
Directory of Open Access Journals (Sweden)
Theoneste Nzayisenga
2014-01-01
Full Text Available While being the dominant source of energy, oil has also brought affluence and power to different societies. Energy produced from oil is fundamental to all parts of society. In the foreseeable future, the majority of energy will still come from oil production. Consequently, reliable methods for forecasting that production are crucial. Petroleum engineers have searched for simple but reliable way to predict oil production for a long time. Many methods have been developed in the latest decades and one common practice is decline curve analysis. Prediction of future production of petroleum wells is important for cost-effective operations of the petroleum industry. This work presents a comparative analysis of methods used to predict the performance of Shuanghe oilfield, China. Using decline curve analysis including three different methods: Arps empirical methods, LL-model and simplified model and the new simplified model, LL-Model, to crosscheck Arps exponential decline model prediction results. The results showed by the comparative analysis of predictions calculated proved LL-model to be the best predictor for Shuanghe oilfield since it takes into account more parameters than the old models used in this work. However, the subsurface information or parameters of the reservoir used in LL-model may not be available every time, therefore Arps models may apply as defined. In Shuanghe oilfield calculated average geological reserves N was estimated at 9449.41 ×104 tons, the average recoverable reserves NR were estimated to 4274.61×104 tons while the water cut was 97% and the water cut predicted by LL-model was 96.7%; not far from water flooding curves value. The exponential decline model showed recoverable reserves NR estimated around 4685.88×104 tons of oil while the decline phase of total development was estimated around 34 years which means that if the actual production conditions remain unchanged, Shuanghe oilfield would continue producing for
U.S. Environmental Protection Agency — The Exposure Forecaster Database (ExpoCastDB) is EPA's database for aggregating chemical exposure information and can be used to help with chemical exposure...
A method of acoustic wave registration and determination their generation region
International Nuclear Information System (INIS)
Kozin, I.D.; Marchenko, M.V.
1998-01-01
Here is presented a method of acoustic wave registration with using of a synchronous LF broadcasting system. This method of detection and determination of underground nuclear explosion location is based on a registration of ionospheric disturbances induced by acoustic waves at the region of LF sign al reflection. The measuring complex created in the institute of the Ionosphere /1/ allows to register amplitude-frequency characteristics of composite signal from synchronous broadcasting net
Measuring longitudinal wave speed in solids: two methods and a half
International Nuclear Information System (INIS)
Fazio, C; Guastella, I; Sperandeo-Mineo, R M; Tarantino, G
2006-01-01
Three methods to analyse longitudinal wave propagation in metallic rods are discussed. Two of these methods also prove to be useful for measuring the sound propagation speed. The experimental results, as well as some interpretative models built in the context of a workshop on mechanical waves at the Graduate School for Pre-Service Physics Teacher Education, Palermo University, are described. Some considerations about observed modifications in trainee teachers' attitudes to utilizing physics experiments to build pedagogical activities are discussed
Multiple travelling wave solutions of nonlinear evolution equations using a unified algebraic method
International Nuclear Information System (INIS)
Fan Engui
2002-01-01
A new direct and unified algebraic method for constructing multiple travelling wave solutions of general nonlinear evolution equations is presented and implemented in a computer algebraic system. Compared with most of the existing tanh methods, the Jacobi elliptic function method or other sophisticated methods, the proposed method not only gives new and more general solutions, but also provides a guideline to classify the various types of the travelling wave solutions according to the values of some parameters. The solutions obtained in this paper include (a) kink-shaped and bell-shaped soliton solutions, (b) rational solutions, (c) triangular periodic solutions and (d) Jacobi and Weierstrass doubly periodic wave solutions. Among them, the Jacobi elliptic periodic wave solutions exactly degenerate to the soliton solutions at a certain limit condition. The efficiency of the method can be demonstrated on a large variety of nonlinear evolution equations such as those considered in this paper, KdV-MKdV, Ito's fifth MKdV, Hirota, Nizhnik-Novikov-Veselov, Broer-Kaup, generalized coupled Hirota-Satsuma, coupled Schroedinger-KdV, (2+1)-dimensional dispersive long wave, (2+1)-dimensional Davey-Stewartson equations. In addition, as an illustrative sample, the properties of the soliton solutions and Jacobi doubly periodic solutions for the Hirota equation are shown by some figures. The links among our proposed method, the tanh method, extended tanh method and the Jacobi elliptic function method are clarified generally. (author)
Chen, Zhongxian; Yu, Haitao; Wen, Cheng
2014-01-01
The goal of direct drive ocean wave energy extraction system is to convert ocean wave energy into electricity. The problem explored in this paper is the design and optimal control for the direct drive ocean wave energy extraction system. An optimal control method based on internal model proportion integration differentiation (IM-PID) is proposed in this paper though most of ocean wave energy extraction systems are optimized by the structure, weight, and material. With this control method, the heavy speed of outer heavy buoy of the energy extraction system is in resonance with incident wave, and the system efficiency is largely improved. Validity of the proposed optimal control method is verified in both regular and irregular ocean waves, and it is shown that IM-PID control method is optimal in that it maximizes the energy conversion efficiency. In addition, the anti-interference ability of IM-PID control method has been assessed, and the results show that the IM-PID control method has good robustness, high precision, and strong anti-interference ability. PMID:25152913
Chen, Zhongxian; Yu, Haitao; Wen, Cheng
2014-01-01
The goal of direct drive ocean wave energy extraction system is to convert ocean wave energy into electricity. The problem explored in this paper is the design and optimal control for the direct drive ocean wave energy extraction system. An optimal control method based on internal model proportion integration differentiation (IM-PID) is proposed in this paper though most of ocean wave energy extraction systems are optimized by the structure, weight, and material. With this control method, the heavy speed of outer heavy buoy of the energy extraction system is in resonance with incident wave, and the system efficiency is largely improved. Validity of the proposed optimal control method is verified in both regular and irregular ocean waves, and it is shown that IM-PID control method is optimal in that it maximizes the energy conversion efficiency. In addition, the anti-interference ability of IM-PID control method has been assessed, and the results show that the IM-PID control method has good robustness, high precision, and strong anti-interference ability.
Synthesis of Numerical Methods for Modeling Wave Energy Converter-Point Absorbers: Preprint
Energy Technology Data Exchange (ETDEWEB)
Li, Y.; Yu, Y. H.
2012-05-01
During the past few decades, wave energy has received significant attention among all ocean energy formats. Industry has proposed hundreds of prototypes such as an oscillating water column, a point absorber, an overtopping system, and a bottom-hinged system. In particular, many researchers have focused on modeling the floating-point absorber as the technology to extract wave energy. Several modeling methods have been used such as the analytical method, the boundary-integral equation method, the Navier-Stokes equations method, and the empirical method. However, no standardized method has been decided. To assist the development of wave energy conversion technologies, this report reviews the methods for modeling the floating-point absorber.
Directory of Open Access Journals (Sweden)
Roman Anatolyevich Yaskevich
2017-12-01
Full Text Available The purpose of the study. Studying the possibility of using mathematical modeling methods for predicting the clinical course of arterial hypertension in women. Materials and methods. 84 women aged 20–60 years (mean age 45,3 years were examined. The survey included clinical, instrumental and laboratory methods of investigation. As a mathematical basis, we used a technique for structuring and analyzing heterogeneous statistical data under conditions of nonparametric uncertainty. Results. In the course of the conducted research on the results of mathematical modeling, using the pattern recognition technique, an individual set of signs (risk factors was formed from the list of indicators that predetermined the risk of development of the predicted state (complicated course of hypertension, which made it possible to construct forecast nomograms, medium and high risk of adverse course of AH in women, which will not only allow us to calculate the degree of risk, but also to determine the parameters of the required change of the level of managed risk factors that determine the presence in a high-risk zone, and, by influencing them to carry out preventive measures. It was found that the clinical course of hypertension in women is influenced by an increase in insulinemia, fasting and postprandial glycemia, BMI, OXC, and blood pressure, that is, a simtomocomplex of the metabolic syndrome. The conclusion. The use of the method of restructuring and analysis of heterogeneous statistical data in conditions of non-parametric uncertainty makes it possible to predict and evaluate the severity of the clinical course of AH in women. The most significant factors affecting the severity of the clinical course of hypertension in men are the indicators of insulinemia, glycemia, BMI, OXC, blood pressure levels.
A new method for wind speed forecasting based on copula theory.
Wang, Yuankun; Ma, Huiqun; Wang, Dong; Wang, Guizuo; Wu, Jichun; Bian, Jinyu; Liu, Jiufu
2018-01-01
How to determine representative wind speed is crucial in wind resource assessment. Accurate wind resource assessments are important to wind farms development. Linear regressions are usually used to obtain the representative wind speed. However, terrain flexibility of wind farm and long distance between wind speed sites often lead to low correlation. In this study, copula method is used to determine the representative year's wind speed in wind farm by interpreting the interaction of the local wind farm and the meteorological station. The result shows that the method proposed here can not only determine the relationship between the local anemometric tower and nearby meteorological station through Kendall's tau, but also determine the joint distribution without assuming the variables to be independent. Moreover, the representative wind data can be obtained by the conditional distribution much more reasonably. We hope this study could provide scientific reference for accurate wind resource assessments. Copyright © 2017 Elsevier Inc. All rights reserved.
Zhaoxuan Li; SM Mahbobur Rahman; Rolando Vega; Bing Dong
2016-01-01
We evaluate and compare two common methods, artificial neural networks (ANN) and support vector regression (SVR), for predicting energy productions from a solar photovoltaic (PV) system in Florida 15 min, 1 h and 24 h ahead of time. A hierarchical approach is proposed based on the machine learning algorithms tested. The production data used in this work corresponds to 15 min averaged power measurements collected from 2014. The accuracy of the model is determined using computing error statisti...
International Nuclear Information System (INIS)
Simon, G.
1990-01-01
Using advanced calculation programs, the inherent behavior and response behavior of structures can reliably be predetermined. In contrast, the dynamic forces affecting a system, in particular unbalances, are often unkown. From balancing of individual rotors, only the vibration path amplitudes at the measuring points used are known. However, these may originate from quite different unbalance distributions. Using probabilistic methods, however, values for the vibrational behavior of the overall structure can be derived from this. (orig.) [de
Jiang, Lijian; Efendiev, Yalchin; Ginting, Victor
2010-01-01
In this paper, we discuss a numerical multiscale approach for solving wave equations with heterogeneous coefficients. Our interest comes from geophysics applications and we assume that there is no scale separation with respect to spatial variables. To obtain the solution of these multiscale problems on a coarse grid, we compute global fields such that the solution smoothly depends on these fields. We present a Galerkin multiscale finite element method using the global information and provide a convergence analysis when applied to solve the wave equations. We investigate the relation between the smoothness of the global fields and convergence rates of the global Galerkin multiscale finite element method for the wave equations. Numerical examples demonstrate that the use of global information renders better accuracy for wave equations with heterogeneous coefficients than the local multiscale finite element method. © 2010 IMACS.
Jiang, Lijian
2010-08-01
In this paper, we discuss a numerical multiscale approach for solving wave equations with heterogeneous coefficients. Our interest comes from geophysics applications and we assume that there is no scale separation with respect to spatial variables. To obtain the solution of these multiscale problems on a coarse grid, we compute global fields such that the solution smoothly depends on these fields. We present a Galerkin multiscale finite element method using the global information and provide a convergence analysis when applied to solve the wave equations. We investigate the relation between the smoothness of the global fields and convergence rates of the global Galerkin multiscale finite element method for the wave equations. Numerical examples demonstrate that the use of global information renders better accuracy for wave equations with heterogeneous coefficients than the local multiscale finite element method. © 2010 IMACS.
Real-space grid implementation of the projector augmented wave method
DEFF Research Database (Denmark)
Mortensen, Jens Jørgen; Hansen, Lars Bruno; Jacobsen, Karsten Wedel
2005-01-01
A grid-based real-space implementation of the projector augmented wave sPAWd method of Blöchl fPhys. Rev. B 50, 17953 s1994dg for density functional theory sDFTd calculations is presented. The use of uniform three-dimensional s3Dd real-space grids for representing wave functions, densities...... valence wave functions that can be represented on relatively coarse grids. We demonstrate the accuracy of the method by calculating the atomization energies of 20 small molecules, and the bulk modulus and lattice constants of bulk aluminum. We show that the approach in terms of computational efficiency...... is comparable to standard plane-wave methods, but the memory requirements are higher....
Spectral element method for wave propagation on irregular domains
Indian Academy of Sciences (India)
Yan Hui Geng
2018-03-14
Mar 14, 2018 ... Abstract. A spectral element approximation of acoustic propagation problems combined with a new mapping method on irregular domains is proposed. Following this method, the Gauss–Lobatto–Chebyshev nodes in the standard space are applied to the spectral element method (SEM). The nodes in the ...
Spectral element method for wave propagation on irregular domains
Indian Academy of Sciences (India)
A spectral element approximation of acoustic propagation problems combined with a new mapping method on irregular domains is proposed. Following this method, the Gauss–Lobatto–Chebyshev nodes in the standard space are applied to the spectral element method (SEM). The nodes in the physical space are ...
Directory of Open Access Journals (Sweden)
Jia Ning
2017-11-01
Full Text Available The uncertainty of wind power results in wind power forecasting errors (WPFE which lead to difficulties in formulating dispatching strategies to maintain the power balance. Demand response (DR is a promising tool to balance power by alleviating the impact of WPFE. This paper offers a control method of combining DR and automatic generation control (AGC units to smooth the system’s imbalance, considering the real-time DR potential (DRP and security constraints. A schematic diagram is proposed from the perspective of a dispatching center that manages smart appliances including air conditioner (AC, water heater (WH, electric vehicle (EV loads, and AGC units to maximize the wind accommodation. The presented model schedules the AC, WH, and EV loads without compromising the consumers’ comfort preferences. Meanwhile, the ramp constraint of generators and power flow transmission constraint are considered to guarantee the safety and stability of the power system. To demonstrate the performance of the proposed approach, simulations are performed in an IEEE 24-node system. The results indicate that considerable benefits can be realized by coordinating the DR and AGC units to mitigate the WPFE impacts.
Zhu, Wenjin; Wang, Jianzhou; Zhang, Wenyu; Sun, Donghuai
2012-05-01
Risk of lower respiratory diseases was significantly correlated with levels of monthly average concentration of SO2; NO2 and association rules have high lifts. In view of Lanzhou's special geographical location, taking into account the impact of different seasons, especially for the winter, the relations between air pollutants and the respiratory disease deserve further study. In this study the monthly average concentration of SO2, NO2, PM10 and the monthly number of people who in hospital because of lower respiratory disease from January 2001 to December 2005 are grouped equidistant and considered as the terms of transactions. Then based on the relational algebraic theory we employed the optimization relation association rule to mine the association rules of the transactions. Based on the association rules revealing the effects of air pollutants on the lower respiratory disease, we forecast the number of person who suffered from lower respiratory disease by the group method of data handling (GMDH) to reveal the risk and give a consultation to the hospital in Xigu District, the most seriously polluted district in Lanzhou. The data and analysis indicate that individuals may be susceptible to the short-term effects of pollution and thus suffer from lower respiratory diseases and this effect presents seasonal.
Forecasting Resource as a Method of Increasing the Security of Technical Devices
Cherepanov, Anatoly P.; Lyapustin, Pavel K.
2017-10-01
The article shows a method of increasing the safe operation of technical devices at various stages of the life cycle according to the proposed classification parameters of the resource by applying the model of resource prediction. The model takes into account the presence of defects, the rate of corrosion and corrosion resistance of the material, the volume of technical diagnosis, the degree of risk in case of failure or damage of technical devices. The article shows the application of the model resource of the technical device from the manufacture to the end of its service life.
Application of the Most Likely Extreme Response Method for Wave Energy Converters: Preprint
Energy Technology Data Exchange (ETDEWEB)
Quon, Eliot; Platt, Andrew; Yu, Yi-Hsiang; Lawson, Michael
2016-07-01
Extreme loads are often a key cost driver for wave energy converters (WECs). As an alternative to exhaustive Monte Carlo or long-term simulations, the most likely extreme response (MLER) method allows mid- and high-fidelity simulations to be used more efficiently in evaluating WEC response to events at the edges of the design envelope, and is therefore applicable to system design analysis. The study discussed in this paper applies the MLER method to investigate the maximum heave, pitch, and surge force of a point absorber WEC. Most likely extreme waves were obtained from a set of wave statistics data based on spectral analysis and the response amplitude operators (RAOs) of the floating body; the RAOs were computed from a simple radiation-and-diffraction-theory-based numerical model. A weakly nonlinear numerical method and a computational fluid dynamics (CFD) method were then applied to compute the short-term response to the MLER wave. Effects of nonlinear wave and floating body interaction on the WEC under the anticipated 100-year waves were examined by comparing the results from the linearly superimposed RAOs, the weakly nonlinear model, and CFD simulations. Overall, the MLER method was successfully applied. In particular, when coupled to a high-fidelity CFD analysis, the nonlinear fluid dynamics can be readily captured.
Forecasting Turbine Icing Events
DEFF Research Database (Denmark)
Davis, Neil; Hahmann, Andrea N.; Clausen, Niels-Erik
2012-01-01
In this study, we present a method for forecasting icing events. The method is validated at two European wind farms in with known icing events. The icing model used was developed using current ice accretion methods, and newly developed ablation algorithms. The model is driven by inputs from the WRF...... mesoscale model, allowing for both climatological estimates of icing and short term icing forecasts. The current model was able to detect periods of icing reasonably well at the warmer site. However at the cold climate site, the model was not able to remove ice quickly enough leading to large ice...
Neural Network Models for Time Series Forecasts
Tim Hill; Marcus O'Connor; William Remus
1996-01-01
Neural networks have been advocated as an alternative to traditional statistical forecasting methods. In the present experiment, time series forecasts produced by neural networks are compared with forecasts from six statistical time series methods generated in a major forecasting competition (Makridakis et al. [Makridakis, S., A. Anderson, R. Carbone, R. Fildes, M. Hibon, R. Lewandowski, J. Newton, E. Parzen, R. Winkler. 1982. The accuracy of extrapolation (time series) methods: Results of a ...
Muñoz-Esparza, Domingo; Kosović, Branko; Jiménez, Pedro A.; Coen, Janice L.
2018-04-01
The level-set method is typically used to track and propagate the fire perimeter in wildland fire models. Herein, a high-order level-set method using fifth-order WENO scheme for the discretization of spatial derivatives and third-order explicit Runge-Kutta temporal integration is implemented within the Weather Research and Forecasting model wildland fire physics package, WRF-Fire. The algorithm includes solution of an additional partial differential equation for level-set reinitialization. The accuracy of the fire-front shape and rate of spread in uncoupled simulations is systematically analyzed. It is demonstrated that the common implementation used by level-set-based wildfire models yields to rate-of-spread errors in the range 10-35% for typical grid sizes (Δ = 12.5-100 m) and considerably underestimates fire area. Moreover, the amplitude of fire-front gradients in the presence of explicitly resolved turbulence features is systematically underestimated. In contrast, the new WRF-Fire algorithm results in rate-of-spread errors that are lower than 1% and that become nearly grid independent. Also, the underestimation of fire area at the sharp transition between the fire front and the lateral flanks is found to be reduced by a factor of ≈7. A hybrid-order level-set method with locally reduced artificial viscosity is proposed, which substantially alleviates the computational cost associated with high-order discretizations while preserving accuracy. Simulations of the Last Chance wildfire demonstrate additional benefits of high-order accurate level-set algorithms when dealing with complex fuel heterogeneities, enabling propagation across narrow fuel gaps and more accurate fire backing over the lee side of no fuel clusters.
Hristova-Veleva, S. M.; Chen, H.; Gopalakrishnan, S.; Haddad, Z. S.
2017-12-01
Tropical cyclones (TCs) are the product of complex multi-scale processes and interactions. The role of the environment has long been recognized. However, recent research has shown that convective-scale processes in the hurricane core might also play a crucial role in determining TCs intensity and size. Several studies have linked Rapid Intensification to the characteristics of the convective clouds (shallow versus deep), their organization (isolated versus wide-spread) and their location with respect to dynamical controls (the vertical shear, the radius of maximum wind). Yet a third set of controls signifies the interaction between the storm-scale and large-scale processes. Our goal is to use observations and models to advance the still-lacking understanding of these processes. Recently, hurricane models have improved significantly. However, deterministic forecasts have limitations due to the uncertainty in the representation of the physical processes and initial conditions. A crucial step forward is the use of high-resolution ensembles. We adopt the following approach: i) generate a high resolution ensemble forecast using HWRF; ii) produce synthetic data (e.g. brightness temperature) from the model fields for direct comparison to satellite observations; iii) develop metrics to allow us to sub-select the realistic members of the ensemble, based on objective measures of the similarity between observed and forecasted structures; iv) for these most-realistic members, determine the skill in forecasting TCs to provide"guidance on guidance"; v) use the members with the best predictive skill to untangle the complex multi-scale interactions. We will report on the first three goals of our research, using forecasts and observations of hurricane Edouard (2014), focusing on RI. We will focus on describing the metrics for the selection of the most appropriate ensemble members, based on applying low-wave number analysis (WNA - Hristova-Veleva et al., 2016) to the observed and
Study on Triopoly Dynamic Game Model Based on Different Demand Forecast Methods in the Market
Directory of Open Access Journals (Sweden)
Junhai Ma
2017-01-01
Full Text Available The impact of inaccurate demand beliefs on dynamics of a Triopoly game is studied. We suppose that all the players make their own estimations on possible demand with errors. A dynamic Triopoly game with such demand belief is set up. Based on this model, existence and local stable region of the equilibriums are investigated by 3D stable regions of Nash equilibrium point. The complex dynamics, such as bifurcation scenarios and route to chaos, are displayed in 2D bifurcation diagrams, in which e1 and α are negatively related to each other. Basins of attraction are investigated and we found that the attraction domain becomes smaller with the increase in price modification speed, which indicates that all the players’ output must be kept within a certain range so as to keep the system stable. Feedback control method is used to keep the system at an equilibrium state.
SKU demand forecasting in the presence of promotions
Gür Ali, Ö.; Sayin, S.; Woensel, van T.; Fransoo, J.C.
2009-01-01
Promotions and shorter life cycles make grocery sales forecasting more difficult, requiring more complicated models. We identify methods of increasing complexity and data preparation cost yielding increasing improvements in forecasting accuracy, by varying the forecasting technique, the input
Application of the Exp-function method to the equal-width wave equation
International Nuclear Information System (INIS)
Biazar, J; Ayati, Z
2008-01-01
In this paper, the Exp-function method is used to find an exact solution of the equal-width wave (EW) equation. The method is straightforward and concise, and its applications are promising. It is shown that the Exp-function method, with the help of symbolic computation, provides a very effective and powerful mathematical tool for solving the EW equation.
Modal Ring Method for the Scattering of Electromagnetic Waves
Baumeister, Kenneth J.; Kreider, Kevin L.
1993-01-01
The modal ring method for electromagnetic scattering from perfectly electric conducting (PEC) symmetrical bodies is presented. The scattering body is represented by a line of finite elements (triangular) on its outer surface. The infinite computational region surrounding the body is represented analytically by an eigenfunction expansion. The modal ring method effectively reduces the two dimensional scattering problem to a one-dimensional problem similar to the method of moments. The modal element method is capable of handling very high frequency scattering because it has a highly banded solution matrix.
Forecasting metal prices: Do forecasters herd?
DEFF Research Database (Denmark)
Pierdzioch, C.; Rulke, J. C.; Stadtmann, G.
2013-01-01
We analyze more than 20,000 forecasts of nine metal prices at four different forecast horizons. We document that forecasts are heterogeneous and report that anti-herding appears to be a source of this heterogeneity. Forecaster anti-herding reflects strategic interactions among forecasters...
Advances in surface wave methods: Cascaded MASW-SASW
Westerhoff, R.S.; Brouwer, J.H.; Meekes, J.A.C.
2005-01-01
The application of the MASW method in areas that show strong lateral variations in subsurface properties is limited. Traditional SASW may yield a better lateral resolution but the dispersion curves (and thus the subsurface models) obtained with the method may be poor. The joint application of MASW
Non-overlapped P- and S-wave Poynting vectors and its solution on Grid Method
Lu, Yong Ming; Liu, Qiancheng
2017-01-01
Poynting vector represents the local directional energy flux density of seismic waves in geophysics. It is widely used in elastic reverse time migration (RTM) to analyze source illumination, suppress low-wavenumber noise, correct for image polarity and extract angle-domain common imaging gather (ADCIG). However, the P and S waves are mixed together during wavefield propagation such that the P and S energy fluxes are not clean everywhere, especially at the overlapped points. In this paper, we use a modified elastic wave equation in which the P and S vector wavefields are naturally separated. Then, we develop an efficient method to evaluate the separable P and S poynting vectors, respectively, based on the view that the group velocity and phase velocity have the same direction in isotropic elastic media. We furthermore formulate our method using an unstructured mesh based modeling method named the grid method. Finally, we verify our method using two numerical examples.
Non-overlapped P- and S-wave Poynting vectors and its solution on Grid Method
Lu, Yong Ming
2017-12-12
Poynting vector represents the local directional energy flux density of seismic waves in geophysics. It is widely used in elastic reverse time migration (RTM) to analyze source illumination, suppress low-wavenumber noise, correct for image polarity and extract angle-domain common imaging gather (ADCIG). However, the P and S waves are mixed together during wavefield propagation such that the P and S energy fluxes are not clean everywhere, especially at the overlapped points. In this paper, we use a modified elastic wave equation in which the P and S vector wavefields are naturally separated. Then, we develop an efficient method to evaluate the separable P and S poynting vectors, respectively, based on the view that the group velocity and phase velocity have the same direction in isotropic elastic media. We furthermore formulate our method using an unstructured mesh based modeling method named the grid method. Finally, we verify our method using two numerical examples.
Forecast Combination under Heavy-Tailed Errors
Directory of Open Access Journals (Sweden)
Gang Cheng
2015-11-01
Full Text Available Forecast combination has been proven to be a very important technique to obtain accurate predictions for various applications in economics, finance, marketing and many other areas. In many applications, forecast errors exhibit heavy-tailed behaviors for various reasons. Unfortunately, to our knowledge, little has been done to obtain reliable forecast combinations for such situations. The familiar forecast combination methods, such as simple average, least squares regression or those based on the variance-covariance of the forecasts, may perform very poorly due to the fact that outliers tend to occur, and they make these methods have unstable weights, leading to un-robust forecasts. To address this problem, in this paper, we propose two nonparametric forecast combination methods. One is specially proposed for the situations in which the forecast errors are strongly believed to have heavy tails that can be modeled by a scaled Student’s t-distribution; the other is designed for relatively more general situations when there is a lack of strong or consistent evidence on the tail behaviors of the forecast errors due to a shortage of data and/or an evolving data-generating process. Adaptive risk bounds of both methods are developed. They show that the resulting combined forecasts yield near optimal mean forecast errors relative to the candidate forecasts. Simulations and a real example demonstrate their superior performance in that they indeed tend to have significantly smaller prediction errors than the previous combination methods in the presence of forecast outliers.
Energy Technology Data Exchange (ETDEWEB)
Suthaker, N.; Tweedie, R. [Thurber Engineering Ltd., Edmonton, AB (Canada)
2009-07-01
Shear wave velocity measurements are an integral part of geotechnical studies for major structures and are an important tool in their design for site specific conditions such as site-specific earthquake response. This paper reported on a study in which shear wave velocities were measured at a proposed petrochemical plant site near Edmonton, Alberta. The proposed site is underlain by lacustrine clay, glacial till and upper Cretaceous clay shale and sandstone bedrock. The most commonly used methods for determining shear wave velocity include crosshole seismic tests, downhole seismic tests, and seismic cone penetration tests (SCPT). This paper presented the results of all 3 methods used in this study and provided a comparison of the various test methods and their limitations. The crosshole test results demonstrated a common trend of increasing shear wave velocity with depth to about 15 m, below which the velocities remained relatively constant. An anomaly was noted at one site, where the shear wave velocity was reduced at a zone corresponding to clay till containing stiff high plastic clay layers. The field study demonstrated that reasonable agreement in shear wave velocity measurements can be made using crosshole, downhole and seismic tests in the same soil conditions. The National Building Code states that the shear wave velocity is the fundamental method for determining site classification, thus emphasizing the importance of obtaining shear wave velocity measurements for site classification. It was concluded that an SCPT program can be incorporated into the field program without much increase in cost and can be supplemented by downhole or crosshole techniques. 5 refs., 2 tabs., 10 figs.
Multiquark masses and wave functions through modified Green's function Monte Carlo method
International Nuclear Information System (INIS)
Kerbikov, B.O.; Polikarpov, M.I.; Shevchenko, L.V.
1987-01-01
The Modified Green's function Monte Carlo method (MGFMC) is used to calculate the masses and ground-state wave functions of multiquark systems in the potential model. The previously developed MGFMC is generalized in order to treat systems containing quarks with inequal masses. The obtained results are presented with the Cornell potential for the masses and the wave functions of light and heavy flavoured baryons and multiquark states (N=6, 9, 12) made of light quarks
A Numerical Method for Blast Shock Wave Analysis of Missile Launch from Aircraft
Directory of Open Access Journals (Sweden)
Sebastian Heimbs
2015-01-01
Full Text Available An efficient empirical approach was developed to accurately represent the blast shock wave loading resulting from the launch of a missile from a military aircraft to be used in numerical analyses. Based on experimental test series of missile launches in laboratory environment and from a helicopter, equations were derived to predict the time- and position-dependent overpressure. The method was finally applied and validated in a structural analysis of a helicopter tail boom under missile launch shock wave loading.
Exact traveling wave solutions of the bbm and kdv equations using (G'/G)-expansion method
International Nuclear Information System (INIS)
Saddique, I.; Nazar, K.
2009-01-01
In this paper, we construct the traveling wave solutions involving parameters of the Benjamin Bona-Mahony (BBM) and KdV equations in terms of the hyperbolic, trigonometric and rational functions by using the (G'/G)-expansion method, where G = G(zeta) satisfies a second order linear ordinary differential equation. When the parameters are taken special values, the Solitary was are derived from the traveling waves. (author)
A comparison of high-order polynomial and wave-based methods for Helmholtz problems
Lieu, Alice; Gabard, Gwénaël; Bériot, Hadrien
2016-09-01
The application of computational modelling to wave propagation problems is hindered by the dispersion error introduced by the discretisation. Two common strategies to address this issue are to use high-order polynomial shape functions (e.g. hp-FEM), or to use physics-based, or Trefftz, methods where the shape functions are local solutions of the problem (typically plane waves). Both strategies have been actively developed over the past decades and both have demonstrated their benefits compared to conventional finite-element methods, but they have yet to be compared. In this paper a high-order polynomial method (p-FEM with Lobatto polynomials) and the wave-based discontinuous Galerkin method are compared for two-dimensional Helmholtz problems. A number of different benchmark problems are used to perform a detailed and systematic assessment of the relative merits of these two methods in terms of interpolation properties, performance and conditioning. It is generally assumed that a wave-based method naturally provides better accuracy compared to polynomial methods since the plane waves or Bessel functions used in these methods are exact solutions of the Helmholtz equation. Results indicate that this expectation does not necessarily translate into a clear benefit, and that the differences in performance, accuracy and conditioning are more nuanced than generally assumed. The high-order polynomial method can in fact deliver comparable, and in some cases superior, performance compared to the wave-based DGM. In addition to benchmarking the intrinsic computational performance of these methods, a number of practical issues associated with realistic applications are also discussed.
Global Energy Forecasting Competition 2012
DEFF Research Database (Denmark)
Hong, Tao; Pinson, Pierre; Fan, Shu
2014-01-01
The Global Energy Forecasting Competition (GEFCom2012) attracted hundreds of participants worldwide, who contributed many novel ideas to the energy forecasting field. This paper introduces both tracks of GEFCom2012, hierarchical load forecasting and wind power forecasting, with details...... on the aspects of the problem, the data, and a summary of the methods used by selected top entries. We also discuss the lessons learned from this competition from the organizers’ perspective. The complete data set, including the solution data, is published along with this paper, in an effort to establish...
Intermittent demand : Linking forecasting to inventory obsolescence
Teunter, Ruud H.; Syntetos, Aris A.; Babai, M. Zied
2011-01-01
The standard method to forecast intermittent demand is that by Croston. This method is available in ERP-type solutions such as SAP and specialised forecasting software packages (e.g. Forecast Pro), and often applied in practice. It uses exponential smoothing to separately update the estimated demand
Hofmann, Douglas C. (Inventor); Wilcox, Brian (Inventor)
2016-01-01
Bulk metallic glass-based strain wave gears and strain wave gear components. In one embodiment, a strain wave gear includes: a wave generator; a flexspline that itself includes a first set of gear teeth; and a circular spline that itself includes a second set of gear teeth; where at least one of the wave generator, the flexspline, and the circular spline, includes a bulk metallic glass-based material.
Damage evaluation by a guided wave-hidden Markov model based method
Mei, Hanfei; Yuan, Shenfang; Qiu, Lei; Zhang, Jinjin
2016-02-01
Guided wave based structural health monitoring has shown great potential in aerospace applications. However, one of the key challenges of practical engineering applications is the accurate interpretation of the guided wave signals under time-varying environmental and operational conditions. This paper presents a guided wave-hidden Markov model based method to improve the damage evaluation reliability of real aircraft structures under time-varying conditions. In the proposed approach, an HMM based unweighted moving average trend estimation method, which can capture the trend of damage propagation from the posterior probability obtained by HMM modeling is used to achieve a probabilistic evaluation of the structural damage. To validate the developed method, experiments are performed on a hole-edge crack specimen under fatigue loading condition and a real aircraft wing spar under changing structural boundary conditions. Experimental results show the advantage of the proposed method.
An Improved Split-Step Wavelet Transform Method for Anomalous Radio Wave Propagation Modelling
Directory of Open Access Journals (Sweden)
A. Iqbal
2014-12-01
Full Text Available Anomalous tropospheric propagation caused by ducting phenomenon is a major problem in wireless communication. Thus, it is important to study the behavior of radio wave propagation in tropospheric ducts. The Parabolic Wave Equation (PWE method is considered most reliable to model anomalous radio wave propagation. In this work, an improved Split Step Wavelet transform Method (SSWM is presented to solve PWE for the modeling of tropospheric propagation over finite and infinite conductive surfaces. A large number of numerical experiments are carried out to validate the performance of the proposed algorithm. Developed algorithm is compared with previously published techniques; Wavelet Galerkin Method (WGM and Split-Step Fourier transform Method (SSFM. A very good agreement is found between SSWM and published techniques. It is also observed that the proposed algorithm is about 18 times faster than WGM and provide more details of propagation effects as compared to SSFM.
Gálvez-Coyt, Gonzalo; Muñoz-Diosdado, Alejandro; Peralta, José; Balderas-López, José; Angulo-Brown, Fernando
2012-06-01
Higuchi's method is a procedure that, if applied appropriately, can determine in a reliable way the fractal dimension D of time series; this fractal dimension permits to characterize the degree of correlation of the series. However, when analyzing some time series with Higuchi's method, there are oscillations at the right-hand side of the graph, which can cause a mistaken determination of the fractal dimension. In this work, an appropriate explanation is given to this type of behaviour. Using the seismogram as a time series and the properties of the P and S waves, it is possible to use the properties of Higuchi's method to previously detect the arrival of the earthquake shacking stage, some seconds in advance, approximately 30-35 s in the case of Mexico City. Thus, we propose the Higuchi's method to characterize and detect the P waves in order to estimate the strength of the forthcoming S waves.
Study of the method to estimate the hydraulic characteristics in rock masses by using elastic wave
International Nuclear Information System (INIS)
Katsu, Kenta; Ohnishi, Yuzo; Nishiyama, Satoshi; Yano, Takao; Ando, Kenichi; Yoshimura, Kimitaka
2008-01-01
In the area of radioactive waste repository, estimating radionuclide migration through the rock mass is an important factor for assessment of the repository. The purpose of this study is to develop a method to estimate hydraulic characteristics of rock masses by using elastic wave velocity dispersion. This method is based on dynamics poroelastic relations such as Biot and BISQ theories. These theories indicate relations between velocity dispersion and hydraulic characteristics. In order to verify the validity of these theories in crystalline rocks, we performed laboratory experiments. The results of experiments show the dependency of elastic wave velocity on its frequency. To test the applicability of this method to real rock masses, we performed in-situ experiment for tuff rock masses. The results of in-situ experiment show the possibility as a practical method to estimate the hydraulic characteristics by using elastic wave velocity dispersion. (author)
A Delphi forecast of technology in education
Robinson, B. E.
1973-01-01
The results are reported of a Delphi forecast of the utilization and social impacts of large-scale educational telecommunications technology. The focus is on both forecasting methodology and educational technology. The various methods of forecasting used by futurists are analyzed from the perspective of the most appropriate method for a prognosticator of educational technology, and review and critical analysis are presented of previous forecasts and studies. Graphic responses, summarized comments, and a scenario of education in 1990 are presented.
Forecasting reliability of transformer populations
Schijndel, van A.; Wetzer, J.; Wouters, P.A.A.F.
2007-01-01
The expected replacement wave in the current power grid faces asset managers with challenging questions. Setting up a replacement strategy and planning calls for a forecast of the long term component reliability. For transformers the future failure probability can be predicted based on the ongoing
Ahmad Kamaruddin, Saadi Bin; Md Ghani, Nor Azura; Mohamed Ramli, Norazan
2013-04-01
The concept of Private Financial Initiative (PFI) has been implemented by many developed countries as an innovative way for the governments to improve future public service delivery and infrastructure procurement. However, the idea is just about to germinate in Malaysia and its success is still vague. The major phase that needs to be given main attention in this agenda is value for money whereby optimum efficiency and effectiveness of each expense is attained. Therefore, at the early stage of this study, estimating unitary charges or materials price indexes in each region in Malaysia was the key objective. This particular study aims to discover the best forecasting method to estimate unitary charges price indexes in construction industry by different regions in the central region of Peninsular Malaysia (Selangor, Federal Territory of Kuala Lumpur, Negeri Sembilan, and Melaka). The unitary charges indexes data used were from year 2002 to 2011 monthly data of different states in the central region Peninsular Malaysia, comprising price indexes of aggregate, sand, steel reinforcement, ready mix concrete, bricks and partition, roof material, floor and wall finishes, ceiling, plumbing materials, sanitary fittings, paint, glass, steel and metal sections, timber and plywood. At the end of the study, it was found that Backpropagation Neural Network with linear transfer function produced the most accurate and reliable results for estimating unitary charges price indexes in every states in central region Peninsular Malaysia based on the Root Mean Squared Errors, where the values for both estimation and evaluation sets were approximately zero and highly significant at p Malaysia. The estimated price indexes of construction materials will contribute significantly to the value for money of PFI as well as towards Malaysian economical growth.
Directory of Open Access Journals (Sweden)
Vladimir Andreyevich Tsybatov
2014-12-01
Full Text Available Fuel and energy complex (FEC is one of the main elements of the economy of any territory over which intertwine the interests of all economic entities. To ensure economic growth of the region should ensure that internal balance of energy resources, which should be developed with account of regional specifics of economic growth and energy security. The study examined the status of this equilibrium, indicating fuel and energy balance of the region (TEB. The aim of the research is the development of the fuel and energy balance, which will allow to determine exactly how many and what resources are not enough to ensure the regional development strategy and what resources need to be brought in. In the energy balances as the focus of displays all issues of regional development, so thermopile is necessary as a mechanism of analysis of current issues, economic development, and in the forward-looking version — as a tool future vision for the fuel and energy complex, energy threats and ways of overcoming them. The variety of relationships in the energy sector with other sectors and aspects of society lead to the fact that the development of the fuel and energy balance of the region have to go beyond the actual energy sector, involving the analysis of other sectors of economy, as well as systems such as banking, budgetary, legislative, tax. Due to the complexity of the discussed problems, the obvious is the need to develop appropriate forecast-analytical system, allowing regional authorities to implement evidence-based predictions of the consequences of management decisions. Multivariant scenario study on development of fuel and energy complex and separately industry, to use the methods of project-based management, harmonized application of state regulation of strategic and market mechanisms on the operational directions of development of fuel and energy complex and separately industry in the economy of the region.
Using inferred probabilities to measure the accuracy of imprecise forecasts
Directory of Open Access Journals (Sweden)
Paul Lehner
2012-11-01
Full Text Available Research on forecasting is effectively limited to forecasts that are expressed with clarity; which is to say that the forecasted event must be sufficiently well-defined so that it can be clearly resolved whether or not the event occurred and forecasts certainties are expressed as quantitative probabilities. When forecasts are expressed with clarity, then quantitative measures (scoring rules, calibration, discrimination, etc. can be used to measure forecast accuracy, which in turn can be used to measure the comparative accuracy of different forecasting methods. Unfortunately most real world forecasts are not expressed clearly. This lack of clarity extends to both the description of the forecast event and to the use of vague language to express forecast certainty. It is thus difficult to assess the accuracy of most real world forecasts, and consequently the accuracy the methods used to generate real world forecasts. This paper addresses this deficiency by presenting an approach to measuring the accuracy of imprecise real world forecasts using the same quantitative metrics routinely used to measure the accuracy of well-defined forecasts. To demonstrate applicability, the Inferred Probability Method is applied to measure the accuracy of forecasts in fourteen documents examining complex political domains. Key words: inferred probability, imputed probability, judgment-based forecasting, forecast accuracy, imprecise forecasts, political forecasting, verbal probability, probability calibration.
Statistical Analysis of Wave Climate Data Using Mixed Distributions and Extreme Wave Prediction
Directory of Open Access Journals (Sweden)
Wei Li
2016-05-01
Full Text Available The investigation of various aspects of the wave climate at a wave energy test site is essential for the development of reliable and efficient wave energy conversion technology. This paper presents studies of the wave climate based on nine years of wave observations from the 2005–2013 period measured with a wave measurement buoy at the Lysekil wave energy test site located off the west coast of Sweden. A detailed analysis of the wave statistics is investigated to reveal the characteristics of the wave climate at this specific test site. The long-term extreme waves are estimated from applying the Peak over Threshold (POT method on the measured wave data. The significant wave height and the maximum wave height at the test site for different return periods are also compared. In this study, a new approach using a mixed-distribution model is proposed to describe the long-term behavior of the significant wave height and it shows an impressive goodness of fit to wave data from the test site. The mixed-distribution model is also applied to measured wave data from four other sites and it provides an illustration of the general applicability of the proposed model. The methodologies used in this paper can be applied to general wave climate analysis of wave energy test sites to estimate extreme waves for the survivability assessment of wave energy converters and characterize the long wave climate to forecast the wave energy resource of the test sites and the energy production of the wave energy converters.
Local Fractional Series Expansion Method for Solving Wave and Diffusion Equations on Cantor Sets
Directory of Open Access Journals (Sweden)
Ai-Min Yang
2013-01-01
Full Text Available We proposed a local fractional series expansion method to solve the wave and diffusion equations on Cantor sets. Some examples are given to illustrate the efficiency and accuracy of the proposed method to obtain analytical solutions to differential equations within the local fractional derivatives.
Time-dependent density-functional theory in the projector augmented-wave method
DEFF Research Database (Denmark)
Walter, Michael; Häkkinen, Hannu; Lehtovaara, Lauri
2008-01-01
We present the implementation of the time-dependent density-functional theory both in linear-response and in time-propagation formalisms using the projector augmented-wave method in real-space grids. The two technically very different methods are compared in the linear-response regime where we...
A cross-correlation method to search for gravitational wave bursts with AURIGA and Virgo
Bignotto, M.; Bonaldi, M.; Camarda, M.; Cerdonio, M.; Conti, L.; Drago, M.; Falferi, P.; Liguori, N.; Longo, S.; Mezzena, R.; Mion, A.; Ortolan, A.; Prodi, G. A.; Re, V.; Salemi, F.; Taffarello, L.; Vedovato, G.; Vinante, A.; Vitale, S.; Zendri, J. -P.; Acernese, F.; Alshourbagy, Mohamed; Amico, Paolo; Antonucci, Federica; Aoudia, S.; Astone, P.; Avino, Saverio; Baggio, L.; Ballardin, G.; Barone, F.; Barsotti, L.; Barsuglia, M.; Bauer, Th. S.; Bigotta, Stefano; Birindelli, Simona; Boccara, Albert-Claude; Bondu, F.; Bosi, Leone; Braccini, Stefano; Bradaschia, C.; Brillet, A.; Brisson, V.; Buskulic, D.; Cagnoli, G.; Calloni, E.; Campagna, Enrico; Carbognani, F.; Cavalier, F.; Cavalieri, R.; Cella, G.; Cesarini, E.; Chassande-Mottin, E.; Clapson, A-C; Cleva, F.; Coccia, E.; Corda, C.; Corsi, A.; Cottone, F.; Coulon, J. -P.; Cuoco, E.; D'Antonio, S.; Dari, A.; Dattilo, V.; Davier, M.; Rosa, R.; Del Prete, M.; Di Fiore, L.; Di Lieto, A.; Emilio, M. Di Paolo; Di Virgilio, A.; Evans, M.; Fafone, V.; Ferrante, I.; Fidecaro, F.; Fiori, I.; Flaminio, R.; Fournier, J. -D.; Frasca, S.; Frasconi, F.; Gammaitoni, L.; Garufi, F.; Genin, E.; Gennai, A.; Giazotto, A.; Giordano, L.; Granata, V.; Greverie, C.; Grosjean, D.; Guidi, G.; Hamdani, S.U.; Hebri, S.; Heitmann, H.; Hello, P.; Huet, D.; Kreckelbergh, S.; La Penna, P.; Laval, M.; Leroy, N.; Letendre, N.; Lopez, B.; Lorenzini, M.; Loriette, V.; Losurdo, G.; Mackowski, J. -M.; Majorana, E.; Man, C. N.; Mantovani, M.; Marchesoni, F.; Marion, F.; Marque, J.; Martelli, F.; Masserot, A.; Menzinger, F.; Milano, L.; Minenkov, Y.; Moins, C.; Moreau, J.; Morgado, N.; Mosca, S.; Mours, B.; Neri, I.; Nocera, F.; Pagliaroli, G.; Palomba, C.; Paoletti, F.; Pardi, S.; Pasqualetti, A.; Passaquieti, R.; Passuello, D.; Piergiovanni, F.; Pinard, L.; Poggiani, R.; Punturo, M.; Puppo, P.; Rapagnani, P.; Regimbau, T.; Remillieux, A.; Ricci, F.; Ricciardi, I.; Rocchi, A.; Rolland, L.; Romano, R.; Ruggi, P.; Russo, G.; Solimeno, S.; Spallicci, A.; Swinkels, B. L.; Tarallo, M.; Terenzi, R.; Toncelli, A.; Tonelli, M.; Tournefier, E.; Travasso, F.; Vajente, G.; van den Brand, J. F. J.; van der Putten, S.; Verkindt, D.; Vetrano, F.; Vicere, A.; Vinet, J. -Y.; Vocca, H.; Yvert, M.
2008-01-01
We present a method to search for transient gravitational waves using a network of detectors with different spectral and directional sensitivities: the interferometer Virgo and the bar detector AURIGA. The data analysis method is based on the measurements of the correlated energy in the network by
Ledoux, L.A.F.; Berkhoff, Arthur P.; Thijssen, J.M.
The Conjugate Gradient Rayleigh method for the calculation of acoustic reflection and transmission at a rough interface between two media was experimentally verified. The method is based on a continuous version of the conjugate gradient technique and plane-wave expansions. We measured the beam