Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks

Liu, Hui; Tian, Huixin; Liang, Xifeng

doi:10.1016/j.apenergy.2015.08.014

Cited by 267 publications

(70 citation statements)

References 35 publications

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“…Li and Liang (2007) carried out a comparison of ElmanNN and RBFNN for flood freeway speed limitation and assumed that the ElmanNN has stronger adaptation and better generalization ability and can build the approximate model more accurately. On the basis of the local feedback network function, the ElmanNN can process the data more precision for nonlinear problem (Liu et al, 2015), which is of great important for the hull resistance prediction. Up to now, hull resistance has been predicted by using radial basis function (RBF) (Huang, Wang, & Yang, 2015;Huang & Yang, 2016), artificial neural networks (Couser, Mason, Mason, Smith, & Konsky, 2004), Holtrop and Mennen's method (Ortigosa, López, & García, 2009), genetic neural network (Chen & Ye, 2009), and BP neural network (Hou, Liu, & Liang, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…On account of the computer simulation, it can predict and analysis the data by learning, control and identification. Now, it has been applied into different kinds of optimization problems (Hu & Balakrishnan, 2005;Liu & Luo, 2005;Liu, Tian, Liang, & Li, 2015;Puig, Witczak, Nejjari, Quevedo, & Korbicz, 2007;Zhang, 2016). The ElmanNN is a typical multi-layer dynamic recurrent neural network, and it has a stronger global stability and a characteristic of time-varying since adding the contest nodes to the hidden nodes of feed-forward network as time delay operator (Ding, Jia, Su, Xu, & Zhang, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Computational fluid dynamics-based hull form optimization using approximation method

Zhang

Tezdogan

et al. 2017

Engineering Applications of Computational Fluid Mechanics

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With the rapid development of the computational technology, computational fluid dynamics (CFD) tools have been widely used to evaluate the ship hydrodynamic performances in the hull forms optimization. However, it is very time consuming since a great number of the CFD simulations need to be performed for one single optimization. It is of great importance to find a high-effective method to replace the calculation of the CFD tools. In this study, a CFD-based hull form optimization loop has been developed by integrating an approximate method to optimize hull form for reducing the total resistance in calm water. In order to improve the optimization accuracy of particle swarm optimization (PSO) algorithm, an improved PSO (IPSO) algorithm is presented where the inertia weight coefficient and search method are designed based on random inertia weight and convergence evaluation, respectively. To improve the prediction accuracy of total resistance, a data prediction method based on IPSO-Elman neural network (NN) is proposed. Herein, IPSO algorithm is used to train the weight coefficients and self-feedback gain coefficient of ElmanNN. In order to build IPSO-ElmanNN model, optimal Latin hypercube design (Opt LHD) is used to design the sampling hull forms, and the total resistance (objective function) of these hull forms are calculated by Reynolds averaged Navier-Stokes (RANS) method. For the purpose of this article, this optimization framework has been employed to optimize two ships, namely, the DTMB5512 and WIGLEY III, and these hull forms are changed by arbitrary shape deformation (ASD) technique. The results show that the optimization framework developed in this study can be used to optimize hull forms with significantly reduced computational effort. ARTICLE HISTORY

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computational fluid dynamics-based hull form optimization using approximation method

Zhang

Tezdogan

et al. 2017

Engineering Applications of Computational Fluid Mechanics

View full text Add to dashboard Cite

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“…Physical forecasting methods utilize physical variables to achieve time series forecasting considering a series of meteorological parameters; therefore, they can perform accurate forecasting [7]. However, they always require more complicated computations and incur a considerable cost in time.…”

Section: Physical Forecasting Methodsmentioning

confidence: 99%

“…Statistical methods construct mathematical and statistical models to conduct time series forecasting and offer better real-time performance [7]. Statistical forecasting methods achieve reduced forecasting errors if the input variables are under normal conditions [8].…”

Section: Statistical Forecasting Methodsmentioning

confidence: 99%

Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting

Liu

2016

Energies

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Abstract:Big data mining, analysis, and forecasting play vital roles in modern economic and industrial fields, especially in the energy system. Inaccurate forecasting may cause wastes of scarce energy or electricity shortages. However, forecasting in the energy system has proven to be a challenging task due to various unstable factors, such as high fluctuations, autocorrelation and stochastic volatility. To forecast time series data by using hybrid models is a feasible alternative of conventional single forecasting modelling approaches. This paper develops a group of hybrid models to solve the problems above by eliminating the noise in the original data sequence and optimizing the parameters in a back propagation neural network. One of contributions of this paper is to integrate the existing algorithms and models, which jointly show advances over the present state of the art. The results of comparative studies demonstrate that the hybrid models proposed not only satisfactorily approximate the actual value but also can be an effective tool in the planning and dispatching of smart grids.

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“…The ARIMA model was also used by Shukur and Lee [14] to show a hybrid wind speed forecasting model with the Kalman filter and an artificial neural network. Liu et al [15] demonstrated a hybrid approach using the secondary decomposition model and Elman neural networks. Fei [16] used a hybrid method that consists of the empirical mode decomposition and multiple-kernel relevance vector regression technologies.…”

Section: Introductionmentioning

confidence: 99%

Wind Energy Potential Assessment and Forecasting Research Based on the Data Pre-Processing Technique and Swarm Intelligent Optimization Algorithms

Wang

2016

Sustainability

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Accurate quantification and characterization of a wind energy potential assessment and forecasting is significant to optimal wind farm design, evaluation and scheduling. However, wind energy potential assessment and forecasting remain difficult and challenging research topics at present. Traditional wind energy assessment and forecasting models usually ignore the problem of data pre-processing as well as parameter optimization, which leads to low accuracy. Therefore, this paper aims to assess the potential of wind energy and forecast the wind speed in four locations in China based on the data pre-processing technique and swarm intelligent optimization algorithms. In the assessment stage, the cuckoo search (CS) algorithm, ant colony (AC) algorithm, firefly algorithm (FA) and genetic algorithm (GA) are used to estimate the two unknown parameters in the Weibull distribution. Then, the wind energy potential assessment results obtained by three data-preprocessing approaches are compared to recognize the best data-preprocessing approach and process the original wind speed time series. While in the forecasting stage, by considering the pre-processed wind speed time series as the original data, the CS and AC optimization algorithms are adopted to optimize three neural networks, namely, the Elman neural network, back propagation neural network, and wavelet neural network. The comparison results demonstrate that the new proposed wind energy assessment and speed forecasting techniques produce promising assessments and predictions and perform better than the single assessment and forecasting components.

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Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks

Cited by 267 publications

References 35 publications

Computational fluid dynamics-based hull form optimization using approximation method

Computational fluid dynamics-based hull form optimization using approximation method

Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting

Wind Energy Potential Assessment and Forecasting Research Based on the Data Pre-Processing Technique and Swarm Intelligent Optimization Algorithms

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