Improved estimation of electricity demand function by using of artificial neural network,principal component analysis and data envelopment analysis

Due to various seasonal and monthly changes in electricity consumption and difficulties in modeling it with the conventional methods, a novel algorithm is proposed in this paper. This study presents an approach that uses Artificial Neural Network (ANN), Principal Component Analysis (PCA), Data Envelopment Analysis (DEA) and ANOVA methods to estimate and predict electricity demand for seasonal and monthly changes in electricity consumption. Pre-processing and post-processing techniques in the data mining field are used in the present study. We analyze the impact of the data pre-processing and post-processing on the ANN performance and a 680 ANN-MLP is constructed for this purpose. DEA is used to compare the constructed ANN models as well as ANN learning algorithm performance. The average, minimum, maximum and standard deviation of mean absolute percentage error (MAPE) of each constructed ANN are used as the DEA inputs. The DEA helps the user to use an appropriate ANN model as an acceptable forecasting tool. In the other words, various error calculation methods are used to find a robust ANN learning algorithm. Moreover, PCA is used as an input selection method, and a preferred time series model is chosen from the linear (ARIMA) and nonlinear models. After selecting the preferred ARIMA model, the Mcleod–Li test is applied to determine the nonlinearity condition. Once the nonlinearity condition is satisfied, the preferred nonlinear model is selected and compared with the preferred ARIMA model, and the best time series model is selected. Then, a new algorithm is developed for the time series estimation; in each case an ANN or conventional time series model is selected for the estimation and prediction. To show the applicability and superiority of the proposed ANN-PCA-DEA-ANOVA algorithm, the data regarding the Iranian electricity consumption from April 1992 to February 2004 are used. The results show that the proposed algorithm provides an accurate solution for the problem of estimating electricity consumption.     

Neural network with multi-trend simulating transfer function for forecasting typhoon wave

This study develops an NN typhoon wave model to accurately and efficiently calculate wave heights at a point of interest. Multi-trend simulating transfer functions were first introduced to exemplify the relationship between wave heights and each conceivable input factor by regressive fitting. The proposed NN–MT model can accurately forecast wave peak with an error of less 1.2 m and with time delay within 3 h and can be extended to cover the station besides the original station of interest.     

A practical ANN model for predicting the PSS of two-way reinforced concrete slabs

This paper aims to develop a practical artificial neural network (ANN) model for predicting the punching shear strength (PSS) of two-way reinforced concrete slabs. In this regard, a total of 218 test results collected from the literature were used to develop the ANN models. Accordingly, the slab thickness, the width of the column section, the effective depth of the slab, the reinforcement ratio, the compressive strength of concrete, and the yield strength of reinforcement were considered as input variables. Meanwhile, the PSS was considered as the output variable. Several ANN models were developed, but the best model with the highest coefficient of determination (R²) and the smallest root mean square errors was retained. The performance of the best ANN model was compared with multiple linear regression and existing design code equations. The comparative results showed that the proposed ANN model was provided the most accurate prediction of PSS of two-way reinforced concrete slabs. The parametric study was carried out using the proposed ANN model to assess the effect of each input parameter on the PSS of two-way reinforced concrete slabs. Finally, a graphical user interface was developed to apply for practical design of PSS of two-way reinforced concrete slabs.

     

Modeling vibration frequencies of annular plates by regression based neural network

In this paper, a regression based artificial neural network model for multi input single output (MISO) systems has been developed. The time devoted in training this model is considerably less in comparison with the traditional ANN model and the number of neurons in the hidden layer can be fixed by choosing a regression polynomial of desired degree. The proposed model has been used and simulated for an example problem of transverse vibrations of plates viz. vibration of circular and elliptic annular plates. There exist nine different boundary conditions for the present example problem which are all simulated using the model. The training and testing with unseen patterns show the efficacy and reliability of the proposed technique for the MISO systems. Comparison of the proposed model with the traditional ANN model shows the former to be better and much efficient.     

An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring

Accurate equipment remaining useful life prediction is critical to effective condition based maintenance for improving reliability and reducing overall maintenance cost. In this paper, an artificial neural network (ANN) based method is developed for achieving more accurate remaining useful life prediction of equipment subject to condition monitoring. The ANN model takes the age and multiple condition monitoring measurement values at the present and previous inspection points as the inputs, and the life percentage as the output. A function generalized from the Weibull failure rate function is used to fit each condition monitoring measurement series for a failure history, and the fitted measurement values are used to form the ANN training set so as to reduce the effects of the noise factors that are irrelevant to the equipment degradation. A validation mechanism is introduced in the ANN training process to improve the prediction performance of the ANN model. The proposed ANN method is validated using real-world vibration monitoring data collected from pump bearings in the field. A comparative study is performed between the proposed ANN method and an adapted version of a reported method, and the results demonstrate the advantage of the proposed method in achieving more accurate remaining useful life prediction.     

On the generalization ability of neural network classifiers

This correspondence presents a method for evaluation of artificial neural network (ANN) classifiers. In order to find the performance of the network over all possible input ranges, a probabilistic input model is defined. The expected error of the output over this input range is taken as a measure of generalization ability. Two essential elements for carrying out the proposed evaluation technique are estimation of the input probability density and numerical integration. A nonparametric method, which depends on the nearest M neighbors, is used to locally estimate the distribution around each training pattern. An orthogonalization procedure is utilized to determine the covariance matrices of local densities. A Monte Carlo method is used to perform the numerical integration. The proposed evaluation technique has been used to investigate the generalization ability of back propagation (BP), radial basis function (RBF) and probabilistic neural network (PNN) classifiers for three test problems     

Performance of artificial neural networks in nearshore wave power prediction

In this paper the assessment of the wave energy potential in nearshore coastal areas is investigated by means of artificial neural networks (ANNs). The performance of the ANNs is compared with in situ measurements and spectral numerical modelling (the conventional tool for wave energy assessment). For this purpose, 13 years of records of two buoys, one offshore and one inshore, with an hourly frequency are used to develop an ANN model for predicting the nearshore wave power. The best suited architecture was selected after assessing the performance of 480 ANN models involving twelve different architectures. The results predicted by the ANN model were compared with the measured data and those obtained by means of the SWAN (Simulating Waves Nearshore) spectral model. The quality in the predictions of the ANN model shows that this type of artificial intelligence models constitutes a powerful tool to forecast the wave energy potential at particular coastal site with great accuracy, and one that overcomes some of the disadvantages of the conventional tools for nearshore wave power prediction.     

Early warning prediction system for high inflation: an elitist neuro-genetic network model for the Indian economy

In this paper, we propose elitist genetic algorithms–based artificial neural network (ANN) model for setting up an early warning system for occurrence of high inflation. The proposed warning system uses values of an appropriate set of economic fundamental variables as input and builds an ANN model for quantifying the possibility of high inflation within a fixed period of time window. Elitism-based generational genetic algorithm is used for optimizing the architecture of the ANN model. We empirically evaluate the proposed neuro-genetic approach to identify the class of leading economic indicators and build an early warning signalling system of an occurrence of high inflation (overall and component inflations) using the data from the Indian economy. We further compare the results of the proposed approach with the commonly used data-driven signals approach. In the empirical studies, we observe promising performance of the proposed neuro-genetic warning system, which is capable of generating accurate early warning signals of an impending high inflation.

     

SAR探测水下运动物体波浪尾迹的模拟研究

水下运动物体可以在海面产生波浪,这种波浪与海洋表面波相互作用改变了海浪谱的高低频分布而形成波浪尾迹。将这样的波-波相互作用处理为对背景海浪谱的扰动,利用海面微波散射的二尺度模型,分析波浪尾迹对雷达散射系数的影响,并给出相应的数值计算模型;对水下不同运动状态物体的波浪用汇源分布法计算出波面函数,将它们分别与一些特定海况的海浪波面函数叠加,并对之进行微波后向散射测量仿真计算,给出波浪尾迹的SAR(Synthetic Aperture Radar、合成孔径雷达)探测图像;再利用二维谱分析技术对模拟SAR图像进行处理,提取波浪尾迹信息。初步结果表明,SAR探测下运动物体是可行的。
     

Propagation and transformation of periodic nonlinear shallow-water waves in basins with selected breakwater systems

This paper describes the development of a Boussinesq three-equation model for simulating propagation and transformation of periodic nonlinear waves (cnoidal waves) in an arbitrary shallow-water basin. The Boussinesq equations in terms of depth-averaged horizontal velocities and free-surface elevation are solved numerically in a curvilinear coordinate system. An Euler’s predictor-corrector finite-difference algorithm is applied for numerical computation. The effects of irregular boundary, non-uniform water depth and coastal structures inside a basin are all included in the model simulation. A second-order cnoidal wave solution for the Boussinesq equations is used as an incident wave condition. A set of open boundary conditions is also applied to effectively transmit waves out of the computational domain. Model tests were conducted by simulating waves propagating past an isolated breakwater. The effect of variable depth was examined with modeling waves over an uneven bottom with convex ramp topography. The overall evolution of wave propagation, diffraction and reflection in coupled harbors with various layouts of inner and outer breakwaters was also studied. Data comparisons reveal that the simulated wave heights agree reasonably well with laboratory measurements, especially in the region of inner basin.     

Implementing the induction-motor drive with four-switch inverter: An application of neural networks

This paper reports a four switch based three-phase voltage source inverter using space vector pulse width modulation (SVPWM), and designed with a three-layer feed forward back propagation based artificial neural network (ANN). The input–output samples, obtained using simulations in Matlab Simulink, were used for the extensive training of the neural network. Matlab interface with National Instruments’ NI-USB-6259 BNC was used for implementing the designed scheme with calculated weights and biases. The designed ANN based SVPWM model receives command voltage and reference speed as the inputs and generates pulse width modulated waves for the four-switch three-phase inverter bridge. The V/f ratio can be controlled by controlling the input parameters of the ANN generating PWM pulses. The simulations and experimental results, and harmonic analysis with the designed ANN structure are presented at different base speeds. The designed model was tested in under modulation, over modulation and unity modulation mode of operation and tuned to give minimum total harmonic distortion. Harmonic results at different modulation indexes are also presented. The ANN based implementation reduces the complexity of control system and overall cost reduction is achieved by the combination of FSTPI and ANN.     

Two-dimensional wave propagation in a nonlinear elastic half-space

Finite amplitude wave propagation in an elastic, isotropic half-space is investigated. A numerical scheme that was previously developed and shown to yield satisfactory accurate results whenever smooth solutions occur is modified here for the cases in which steep solutions are obtained. The stability analysis of the proposed numerical procedure is carried out, and the stability criteria are given in terms of the spectral radii of the matrices involved in the equations of motion. The hyperbolicity conditions of the equations of motion are derived and shown to impose restrictions on the possible values of displacement gradients so that the range of variation of the strength of the applied load is limited. As a first chek of the accuracy of the numerical results, a propagating shock wave is produced numerically and compared with the analytical solution. In a second check, propagating circularly polarized waves are numerically simulated and compared with the corresponding analytical solution. In each case good agreement is obtained. For the “quadratic material” adopted in this paper, it is shown that a compressive normal line force yields propagating pulses having larger amplitudes, broader widths and larger arrival times, as compared with those caused by a tensile one. The linear response is also shown for comparison.     

Travelling waves in a reaction-diffusion model for electrodeposition

In this paper we consider an analytical and numerical study of a reaction-diffusion system for describing the formation of transition front waves in some electrodeposition (ECD) experiments. Towards this aim, a model accounting for the coupling between morphology and composition of one chemical species adsorbed at the surface of the growing cathode is addressed. Through a phase-space analysis we prove the existence of travelling waves, moving with specific wave speed. The numerical approximation of the PDE system is performed by the Method of Lines (MOL) based on high order space semi-discretization by means of the Extended Central Difference Formulae (D2ECDF) introduced in [1]. First of all, to show the advantage of the proposed schemes, we solve the well-known Fisher scalar equation, focusing on the accurate approximation of the wave profile and of its speed. Hence, we provide numerical simulations for the electrochemical reaction-diffusion system and we show that the results obtained are qualitatively in good agreement with experiments for the electrodeposition of Au-Cu alloys.     

四阶高分辨率熵相容算法

针对一维Burgers方程和一维Euler方程组的数值求解问题,提出了一种四阶高分辨率熵相容算法。新算法时间方向采用半离散方式,空间方向应用四阶中心加权基本无振荡(CWENO)重构方法,数值通量引入Ismail通量函数,将新的四阶算法应用于静态激波问题、激波管问题以及强稀疏波问题的数值求解中,并将所得结果同准确解以及已有算法所得结果进行了分析与比较。数值结果表明:新算法计算结果正确、分辨率高,能够准确捕捉激波及稀疏波,并能有效避免膨胀激波的产生。新算法适用于准确解决一维Burgers方程和一维Euler方程组的数值求解问题。     

基于波环粒子的实时水波仿真方法

为了降低水波模拟过程中的计算成本并提高其扩散现象的逼真度，提出一种基于波环粒子包的实时二维平面水波仿真方法。该方法采用波环粒子为基本计算单元，粒子内部继承“波包”的概念，使用多个频段水波叠加的方式再现水波视觉效果。在计算水波反射过程时，通过添加镜像波源的形式减少碰撞计算，避免复杂几何判定。为适应不同硬件的计算性能差异，该方法提供额外的计算精度参数，可针对不同硬件计算能力调节水波反射计算复杂度。实验结果表明，该方法可使用较少的粒子模拟出较为真实的水波运动，且避免了碰撞反射后水波断裂的问题。在相同硬件平台上的性能测试显示，所提波环仿真方法的渲染帧率比传统波包算法高出至少60%，在一些水波状态特别复杂的情况下可达到400%以上的加速效果。     

A new algorithm for the extraction of the surface waves for the Green''''s function in layered dielectrics

There exist the complicated waveguide modes as well as the surface waves in the electromagnetic field induced by a horizontal electric dipole in layered lossless dielectrics between two ground planes. In spectral domain, all these modes can be characterized by the rational parts with the real poles of the vector and scalar potentials. The accurate extraction of these modes plays an important role in the evaluation of the Green's function in spatial domain. In this paper, a new algorithm based on rational approximation is presented, which can accurately extract all the real poles and the residues of each pole simultaneously. Thus, we can get all the surface wave modes and waveguide modes, which is of great help to the calculation of the spatial domain Green's function. The numerical results demonstrated the accuracy and efficiency of the proposed method.     

A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data

A suitable combination of linear and nonlinear models provides a more accurate prediction model than an individual linear or nonlinear model for forecasting time series data originating from various applications. The linear autoregressive integrated moving average (ARIMA) and nonlinear artificial neural network (ANN) models are explored in this paper to devise a new hybrid ARIMA–ANN model for the prediction of time series data. Many of the hybrid ARIMA–ANN models which exist in the literature apply an ARIMA model to given time series data, consider the error between the original and the ARIMA-predicted data as a nonlinear component, and model it using an ANN in different ways. Though these models give predictions with higher accuracy than the individual models, there is scope for further improvement in the accuracy if the nature of the given time series is taken into account before applying the models. In the work described in this paper, the nature of volatility was explored using a moving-average filter, and then an ARIMA and an ANN model were suitably applied. Using a simulated data set and experimental data sets such as sunspot data, electricity price data, and stock market data, the proposed hybrid ARIMA–ANN model was applied along with individual ARIMA and ANN models and some existing hybrid ARIMA–ANN models. The results obtained from all of these data sets show that for both one-step-ahead and multistep-ahead forecasts, the proposed hybrid model has higher prediction accuracy.     

Online implementation of an adaptive calibration technique for displacement measurement using LVDT

The paper presents the design and validation of an online intelligent displacement measurement technique with Linear Variable Differential Transformer (LVDT) using Artificial Neural Network (ANN). The objectives of the proposed work are to design a calibration technique using an optimised neural network model such that it a) produces an output which is linear for the full scale of input range, b) makes the output independent of the variations in supply frequency, the physical parameters of the LVDT, and ambient temperature. The output of an LVDT is converted to a DC signal by using a rectifier circuit. The rectified output is further amplified using a differential amplifier. This voltage signal is acquired onto a computer for further processing using an ANN. The optimisation of the ANN is carried out to find the minimum number of hidden layers along with the number of neurones in each layer to give least Mean Square Error (MSE) and Regression (R) nearing to one. This optimisation is done considering various schemes of ANN, training algorithms, and the transfer function of neurones. Once the ANN model is designed, it is subjected to test with both simulated data and experimental validation. The results confirm the successful achievement of the objectives of this paper and thus avoiding the need for repeated calibration.     

A new matching strategy for content based image retrieval system

Adopting effective model to access the desired images is essential nowadays with the presence of a huge amount of digital images. The present paper introduces an accurate and rapid model for content based image retrieval process depending on a new matching strategy. The proposed model is composed of four major phases namely: features extraction, dimensionality reduction, ANN classifier and matching strategy. As for the feature extraction phase, it extracts a color and texture features, respectively, called color co-occurrence matrix (CCM) and difference between pixels of scan pattern (DBPSP). However, integrating multiple features can overcome the problems of single feature, but the system works slowly mainly because of the high dimensionality of the feature space. Therefore, the dimensionality reduction technique selects the effective features that jointly have the largest dependency on the target class and minimal redundancy among themselves. Consequently, these features reduce the calculation work and the computation time in the retrieval process. The artificial neural network (ANN) in our proposed model serves as a classifier so that the selected features of query image are the input and its output is one of the multi classes that have the largest similarity to the query image. In addition, the proposed model presents an effective feature matching strategy that depends on the idea of the minimum area between two vectors to compute the similarity value between a query image and the images in the determined class. Finally, the results presented in this paper demonstrate that the proposed model provides accurate retrieval results and achieve improvement in performance with significantly less computation time compared with other models.     

High-resolution satellite measurements of coastal wind field and sea state

Methods to derive wind speed and sea state by simple empirical models from synthetic aperture radar (SAR) data are presented and applied for use in high-resolution numerical modelling for coastal applications. The new radar satellite, TerraSAR-X (TS-X), images the surface of the sea with a high resolution up to 1 m. Therefore, not only wind information and integrated sea state parameters but also individual ocean waves with wavelengths down to 30 m are detectable. Two-dimensional information on the ocean surface retrieved using TS-X data is validated for different oceanographic applications: derivation of finely resolved wind fields (XMOD algorithm) and integrated sea state parameters (XWAVE algorithm). Both algorithms are capable of taking into account fine-scale effects in coastal areas. Wind and sea state information retrieved from SAR data are applied as the input for a wave numerical spectral model (wind forcing and boundary condition) running at a fine spatial horizontal resolution of 100 m. Results are compared to collocated buoy measurements. Studies are carried out for varying wind speeds and comparisons against wave height, simulated using original TS-X-derived wind data, showing the sensitivity of waves to local wind variation and thus the importance of local wind effects on wave behaviour in coastal areas. Examples for the German Bight (North Sea) are shown. The TS-X satellite scenes render well-developed ocean wave patterns of developed swell at the sea surface. Refraction of individual long swell waves at a water depth shallower than about 70 m, caused by the influence of underwater topography in coastal areas, is imaged on the radar scenes. A technique is developed for tracking wave rays depending on changes in swell wavelength and direction. We estimate the wave energy flux along wave tracks from deep water to the coastline based on SAR information: wave height and wavelength are derived from TS-X data.