A hybrid computational approach to estimate solar global radiation: An empirical evidence from Iran

Mostafavi, Elham Sadat; Ramiyani, Sara Saeidi; Rahim, Sarvar; Moud, Hashem Izadi; Mousavi, Seyyed Mohammad

doi:10.1016/j.energy.2012.11.023

Cited by 70 publications

(19 citation statements)

References 33 publications

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“…Mostafavi et al (2013) developed a hybrid approach for estimation of the solar global radiation by combining genetic programming (GP) and simulated annealing (SA). They also performed a sensitivity analysis to assess the influence of different meteorological parameters on solar radiation estimation.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: A hybrid SVM-FFA method for prediction of monthly mean global solar radiation

Shamshirband

Mohammadi

Chong

et al. 2015

Theor Appl Climatol

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In this study, a hybrid support vector machine-firefly optimization algorithm (SVM-FFA) model is proposed to estimate monthly mean horizontal global solar radiation (HGSR). The merit of SVM-FFA is assessed statistically by comparing its performance with three previously used approaches. Using each approach and long-term measured HGSR, three models are calibrated by considering different sets of meteorological parameters measured for Bandar Abbass situated in Iran. It is found that the model (3) utilizing the combination of relative sunshine duration, difference between maximum and minimum temperatures, relative humidity, water vapor pressure, average temperature, and extraterrestrial solar radiation shows superior performance based upon all approaches. Moreover, the extraterrestrial radiation is introduced as a significant parameter to accurately estimate the global solar radiation. The survey results reveal that the developed SVM-FFA approach is greatly capable to provide favorable predictions with significantly higher precision than other examined techniques. For the SVM-FFA (3), the statistical indicators of mean absolute percentage error (MAPE), root mean square error (RMSE), relative root mean square error (RRMSE), and coefficient of determination (R 2 ) are 3.3252 %, 0.1859 kWh/m 2 , 3.7350 %, and 0.9737, respectively which according to the RRMSE has an excellent performance. As a more evaluation of SVM-FFA (3), the ratio of estimated to measured values is computed and found that 47 out of 48 months considered as testing data fall between 0.90 and 1.10. Also, by performing a further verification, it is concluded that SVM-FFA (3) offers absolute superiority over the empirical models using relatively similar input parameters. In a nutshell, the hybrid SVM-FFA approach would be considered highly efficient to estimate the HGSR.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: A hybrid SVM-FFA method for prediction of monthly mean global solar radiation

Shamshirband

Mohammadi

Chong

et al. 2015

Theor Appl Climatol

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“…A methodology similar to that successfully used in previously published studies was considered to derive a precise MGGP-based prediction model for the electricity demand (Azadeh et al 2008b;Mostafavi et al 2013b). The steps followed to derive the model were as follows:…”

Section: Methodsmentioning

confidence: 99%

“…GP has several advantages over the conventional and ANN techniques. A notable feature of GP is that it can produce practical prediction equations without a need to predefine the form of the existing relationship (Tay and Ho 2008;Alavi et al 2011a, b;Heavey 2011, 2012;Alavi 2011, 2013;Alavi and Gandomi 2012;Mostafavi et al 2013b). GP and its variants have been shown to be powerful tools for the electricity demand prediction (Lee et al 1997;Bhattacharya et al 2001).…”

Section: Introductionmentioning

confidence: 97%

Towards estimation of electricity demand utilizing a robust multi-gene genetic programming technique

2015

Self Cite

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Multi-gene genetic programming (MGGP) is a new nonlinear system modeling approach that integrates the capabilities of standard genetic programming and classical regression. This paper deals with the application of this robust technique for the prediction of annual electricity demand in Thailand. The predictor variables included in the analysis were population, gross domestic product, stock index, and total revenue from exporting industrial products. Several statistical criteria were used to verify the validity of the model. A sensitivity analysis was performed to evaluate the contributions of the input features. The correlation coefficients between the measured and predicted electricity demand values are equal to 0.999 and 0.997 for the calibration and testing data sets, respectively. In addition to its high accuracy, MGGP outperforms regression and other powerful soft computing-based techniques.

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“…The first problem is to formulate a prediction model for the electricity demand in Thailand from 1986 to 2010 25,16,22,23 . The sample includes four input attributes, and the electricity consumption is the final output.…”

Section: Methodsmentioning

confidence: 99%

“…Existing algorithms, including standard GEP 25 , hybrid genetic programming-simulated annealing (GSA) 23 , neural network (NN) 22 and multiple linear regression (MLR) 16 , are employed for the comparison purpose. Table 3 presents the average approximation accuracy of the proposed algorithm and conventional methods over 30 runs.…”

Section: Performance Analysis Of the Ogep Algorithmmentioning

confidence: 99%

A hybrid gene expression programming algorithm based on orthogonal design

Yang¹,

Ma²

2016

IJCIS

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The last decade has witnessed a great interest on the application of evolutionary algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO) and gene expression programming (GEP), for optimization problems. This paper presents a hybrid algorithm by combining the GEP algorithm and the orthogonal design method. A multiple-parent crossover operator is introduced for the chromosome reproduction using the orthogonal design method. In addition, an evolutionary stable strategy is also employed to maintain the population diversity during the evolution. The efficiency of the proposed algorithm is evaluated using three benchmark problems. The results demonstrate that the proposed hybrid algorithm has a better generalization ability compared to conventional algorithms.

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A hybrid computational approach to estimate solar global radiation: An empirical evidence from Iran

Cited by 70 publications

References 33 publications

RETRACTED ARTICLE: A hybrid SVM-FFA method for prediction of monthly mean global solar radiation

RETRACTED ARTICLE: A hybrid SVM-FFA method for prediction of monthly mean global solar radiation

Towards estimation of electricity demand utilizing a robust multi-gene genetic programming technique

A hybrid gene expression programming algorithm based on orthogonal design

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