Optimum prediction is a difficult problem, because there are no optimal models for all forecasting problems. In this paper, the authors attempt to find the high precision prediction for grey forecasting model (GM). Co...
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Optimum prediction is a difficult problem, because there are no optimal models for all forecasting problems. In this paper, the authors attempt to find the high precision prediction for grey forecasting model (GM). Considering that chaotic particle swarm optimization algorithm (CPSO) will not get into local optimum and is easy to implement, the paper develops an approach for grey forecasting model, which is particularly suitable for small sample forecasting, based on chaotic particle swarm optimization and optimal input subset which is a new concept. The input subset of traditional time series consists of the whole original data, but the whole original does not always reflect the internal regularity of time series, so the new optimal subset method is proposed to better reflect the internal characters of time series and improve the prediction precision. The numerical simulation result of financial revenue demonstrates that developed algorithm provides very remarkable results compared to traditional grey forecasting model for small dataset forecasting. (C) 2010 Elsevier Ltd. All rights reserved.
The study first applies a three-dimensional model to analyze the cell performance of PEMFCs using rectangular cylinders with various numbers transversely inserted at the axis in the channel, and finds the higher perfo...
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The study first applies a three-dimensional model to analyze the cell performance of PEMFCs using rectangular cylinders with various numbers transversely inserted at the axis in the channel, and finds the higher performance with reasonable pressure drop. The Taguchi optimization methodology is then combined with the three-dimensional PEMFC model to determine the optimal combination of five primary operating parameters for the best arrangement of the rectangular cylinders in the channel. The results indicate that the optimal combination factor is a cell temperature of 313 K, an anode humidification temperature of 333 K, a cathode humidification temperature of 333 K, a hydrogen stoichiometric flow ratio of 1.9, and an oxygen stoichiometric flow ratio of 2.7. This study also examines the pressure drop for the channels with rectangular cylinders transversely inserted. Using experimental data verifies the numerical results of the flow field design with rectangular cylinders. (C) 2011 Elsevier Ltd. All rights reserved.
This paper introduces a proposed approach to estimate the optimalparameters of the photovoltaic (PV) modules using in-field outdoor measurements and manufacturers' datasheet as well as employing the nonlinear lea...
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This paper introduces a proposed approach to estimate the optimalparameters of the photovoltaic (PV) modules using in-field outdoor measurements and manufacturers' datasheet as well as employing the nonlinear least-squares fitting algorithm. The main goal is to determine the optimal parameter values of the implemented model which are: series resistance, reverse saturation current, photocurrent, ideality factor and shunt resistance in case of the five parameters model. A Microsoft Excel spreadsheet is developed in order to perform modeling and analysis of the parameters analytical initial values using manufacturer datasheet specifications regarding to the changing in solar irradiance and ambient temperature. Then, the sum of the squared residuals between in-field measured and simulated data are calculated and minimized using Excel solver in order to obtain the optimal values of the parameters simultaneously, to describe the best fit for the outdoor measured data. The proposed approach is used to find the optimalparameters of the PV module TRINA TSM-295 using an array tester. The convergence confidences of the estimated parameters are presented and assessed in an easy way. This approach allows all parameters to be optimized, simultaneously. The results are verified and compared with other research studies for different PV cell technologies. The obtained results are useful for the tested PV module manufacturer and assess the performance of the products in different weather conditions.
For a better use of wind energy, the accurate selection of the wind speed distributions that best represents the regarding wind regime's characteristics is essential. The Weibull distribution is the most common, b...
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For a better use of wind energy, the accurate selection of the wind speed distributions that best represents the regarding wind regime's characteristics is essential. The Weibull distribution is the most common, but this model is not always the most suitable. Therefore, in order to obtain more reliable information, the evaluation of different distributions becomes necessary. Another crucial step is the estimation of the parameters that govern these distributions because the accuracy of these estimates directly affects the energy generation calculations. In the last few years, different optimization methods have been used for this purpose. However, the applications of these methods are focused on conventional two-parameter distributions, such as Weibull and Lognormal. Futhermore, different authors report that there is a lack of studies that use optimization methods for this purpose. In this paper, four metaheuristic optimization algorithms (MOA)-namely, Migrating Birds Optimization (MBO), Imperialist Competitive Algorithm (ICA), Harmony Search (HS) and Cuckoo Search (CS)-are used to fit 11 distributions in two Brazillian regions. Thus, this work expands the application of the MOA to beyond the conventional distributions and applies, for the first time, the MBO and ICA in estimating the parameters of wind speed distributions, thereby introducing new ways to optimize the use of wind resources. The fits obtained by the MOA were compared with those obtained by the method Maximum Likelihood estimation (MLE). Gamma Generalized and Extended Generalized Lindley distributions presented the best fits, and the MOA outperformed the MLE because the global score values obtained were smaller.
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