Long-term wind speed prediction based on optimized support vector regression

Osama, Sarah; Darwish, Ashraf; Houssein, Essam H.; Hassanien, Aboul Ella; Fahmy, Aly A.; Mahrous, A.

doi:10.1109/intelcis.2017.8260035

Cited by 10 publications

(4 citation statements)

References 15 publications

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“…Mimicking ethological, biological, or physics phenomena is the main means of meta-heuristic algorithms in solving optimization problems [21]. The algorithms applied to SVR include GA [15], [22], FA [4], gray wolf optimization (GWO) [23], PSO [10], ALO [11], DA [12], SSA [13], whale optimization algorithm (WOA) [24], [25], elephant herding algorithm (EHO) [26], simulated annealing (SA) [27], etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Parameter Optimization of Support Vector Regression Using Henry Gas Solubility Optimization Algorithm

Cao

Liu

2020

IEEE Access

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Support vector regression (SVR) is one of the most powerful and widely used machine learning algorithms regarding prediction. The kernel type, penalty factor and other parameters influence the efficiency and performance of SVR deeply. The optimization of these parameters is held a hot issue. In this work, we propose a SVR based prediction approach using henry gas solubility optimization algorithm (HGSO), which is a recent meta-heuristic algorithm inspired by Henry's law. First, SVR parameters are randomly generated in some certain ranges to form parameter population. Second, the prediction accuracies (PAs) are obtained using the population and SVR. Thirdly, the population and optimal SVR parameters are updated via PAs and HGSO. We repeat the second and third steps until the cutoff conditions are met. Ten low-and high-dimensional benchmark data sets are utilized to assess the prediction accuracy, convergence performance and computational complexity of the presented approach and other well-known algorithms. The experimental results reveal that our approach has the optimum comprehensive performance.

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Section: Literature Reviewmentioning

confidence: 99%

Parameter Optimization of Support Vector Regression Using Henry Gas Solubility Optimization Algorithm

Cao

Liu

2020

IEEE Access

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“…The model proposed makes full use of the ability of SVR to achieve classification and generalization in the case of small sample size and the optimization ability of the WOA algorithm. 39 The flow of the model suggested is shown in Figure 2, and the main steps include the following:…”

Section: Fatigue Life Prediction Model Based On Woa-svrmentioning

confidence: 99%

Fatigue life prediction of welded joints based on improved support vector regression model under two‐level loading

Zou

Yang

et al. 2023

Fatigue Fract Eng Mat Struct

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To meet the lightweight needs of modern machinery equipment, welded structure is widely used in practical engineering. During the service period, the welded structure is often subjected to multilevel variable amplitude loading, and fatigue failure is easy to occur. Due to the complexity and stochasticity of fatigue process, the traditional life prediction models cannot meet the demand of practical engineering. In this work, a novel prediction model based on the whale optimization algorithm (WOA) together with the support vector regression (SVR), namely, a WOA‐SVR model, is established. It combines the advantages of WOA and SVR for global searching optimization and high prediction accuracy dealing with small sample size. By taking into account the sequence of loadings and the interaction effect between loadings, the proposed WOA‐SVR model could overcome the deficiencies and shortcomings in existing models. Experiment results show that WOR‐SVR has better prediction performance than five models.

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“…A major drawback of the use of fossil fuel is its negative environmental effects; therefore, alternative renewable energy sources (RESs) are used as an alternative clean and affordable energy source. There are seven types of RESs namely, biomass (López et al, 2008), geothermal (Houssein, 2019), wind (Osama et al, 2017), solar (Hocaoglu, 2011;Munshi & Yasser, 2017), hydroelectric (Bagher et al, 2015), hydrogen (Dunn, 2002), and tide (Egbert & Ray, 2000). Solar energy is considered one of the most important sources of renewable energy, as the sun plays a significant role in it's generation.…”

Section: Introductionmentioning

confidence: 99%

An efficient Equilibrium Optimizer for parameters identification of photovoltaic modules

Houssein

Nageh

Elaziz

et al. 2021

PeerJ Computer Science

Self Cite

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The use of solar photovoltaic systems (PVs) is increasing as a clean and affordable source of electric energy. The Pv cell is the main component of the PV system. To improve the performance, control, and evaluation of the PV system, it is necessary to provide accurate design and to define the intrinsic parameters of the solar cells. There are many methods for optimizing the parameters of the solar cells. The first class of methods is called the analytical methods that provide the model parameters using datasheet information or I–V curve data. The second class of methods is the optimization-based methods that define the problem as an optimization problem. The optimization problem objective is to minimize the error metrics and it is solved using metaheuristic optimization algorithms. The third class of methods is composed of a hybrid of both the analytical and the metaheuristic approaches, some parameters are computed by the analytical approach and the rest are found using metaheuristic optimization algorithms. Research in this area faces two challenges; (1) finding an optimal model for the parameters of the solar cells and (2) the lack of data about the photovoltaic cells. This paper proposes an optimization-based algorithm for accurately estimating the parameters of solar cells. It is using the Improved Equilibrium Optimizer algorithm (IEO). This algorithm is improved using the Opposition Based Learning (OBL) at the initialization phase of EO to improve its population diversity in the search space. Opposition-based Learning (OBL) is a new concept in machine learning inspired by the opposite relationship among entities. There are two common models for solar cells; the single diode model (SDM) and double diode model (DDM) have been used to demonstrate the capabilities of IEO in estimating the parameters of solar cells. The proposed methodology can find accurate solutions while reducing the computational cost. Compared to other existing techniques, the proposed algorithm yields less mean absolute error. The results were compared with seven optimization algorithms using data of different solar cells and PV panels. The experimental results revealed that IEO is superior to the most competitive algorithms in terms of the accuracy of the final solutions.

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Long-term wind speed prediction based on optimized support vector regression

Cited by 10 publications

References 15 publications

Parameter Optimization of Support Vector Regression Using Henry Gas Solubility Optimization Algorithm

Parameter Optimization of Support Vector Regression Using Henry Gas Solubility Optimization Algorithm

Fatigue life prediction of welded joints based on improved support vector regression model under two‐level loading

An efficient Equilibrium Optimizer for parameters identification of photovoltaic modules

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