ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment

Quej, Victor H.; Almorox, Javier; Arnaldo, Javier; Saito, Laurel

doi:10.1016/j.jastp.2017.02.002

Cited by 242 publications

(92 citation statements)

References 35 publications

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“…Each layer is interconnected by weights W ij and W jk , every unit sums its inputs and adds a bias or threshold term to the sum, and this sum is then transformed by an activation function to produce an output (Quej et al 2017). Logistic sigmoid function (Eq.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Oxygen transfer study and modeling of plunging hollow jets

2018

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Experiments were performed to study the effect of jet angle of hollow jet aerators on oxygen transfer of water. Three jet angles of 30°, 45° and 60° were used to produce hollow jets impingement on the water in the pool. Results from experimental study showed a significant effect of hollow jet angle on volumetric oxygen transfer coefficient (K L a) at higher jet velocities. Standard oxygen transfer efficiency (SOTE) of 60° hollow jet aerators is observed higher with low-kinetic-power jets, but at higher kinetic power, 30° hollow jet aerator is observed to be the best in terms of SOTE. Moreover, modeling performance of the experimental data is evaluated by training and testing of the models, and empirical equations derived from multiple linear and multiple nonlinear regression techniques are compared with artificial neural network approach. The results of the present study are also compared with the previous studies of plunging jet aerators.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Oxygen transfer study and modeling of plunging hollow jets

2018

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“…, for(i = 1, 2.., ..) Q 2,i = µ B i−2 (x 1 ), for(i = 1, 2.., ..) . (1) There are different types of membership functions, and the following bell function has been widely used with successful applications in previous research:…”

Section: Fuzzy Methodsmentioning

confidence: 99%

“…In addition, the proposed hybrid model showed outstanding performance for the development of a forecasting model for the solar radiation value, which is essential for the decision-makers to draw a future plan for generating renewable energy based solar radiation.The Sun is considered a fundamental resource for most physical and chemical processes on Earth. Thus, processes related to the Sun are important for researchers [1,2]. The application of solar energy for different aims as a renewable energy source is an important priority for researchers [3].…”

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confidence: 99%

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Accuracy Enhancement for Zone Mapping of a Solar Radiation Forecasting Based Multi-Objective Model for Better Management of the Generation of Renewable Energy

et al. 2019

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The estimation of solar radiation for planning current and future periods in different fields, such as renewable energy generation, is very important for decision makers. The current study presents a hybrid model structure based on a multi-objective shark algorithm and fuzzy method for forecasting and generating a zone map for solar radiation as an alternative solution for future renewable energy production. The multi-objective shark algorithm attempts to select the best input combination for solar radiation (SR) estimation and the optimal value of the adaptive neuro-fuzzy inference system (ANFIS) parameter, and the power parameter of the inverse distance weight (IDW) is computed. Three provinces in Iran with different climates and air quality index conditions have been considered as case studies for this research. In addition, comparative analysis has been carried out with other models, including multi-objective genetic algorithm-ANFIS and multi-objective particle swarm optimization-ANFIS. The Taguchi model is used to obtain the best value of random parameters for multi-objective algorithms. The comparison of the results shows that the relative deviation index (RDI) of the distributed solutions in the Pareto front based multi-objective shark algorithm has the lowest value in the spread index, spacing metric index, favorable distribution, and good diversity. The generated Pareto solutions based on the multi-objective shark algorithm are compared to those based on the genetic algorithm and particle swarm algorithm and found to be the optimal and near ideal solutions. In addition, the determination of the best solution based on a multi-criteria decision model enables the best input to the model to be selected based on different effective parameters. Three different performance indices have been used in this study, including the root mean square error, Nash–Sutcliffe efficiency, and mean absolute error. The generated zone map based on the multi-objective shark algorithm-ANFIS highly matches with the observed data in all zones in all case studies. Additionally, the analysis shows that the air quality index (AQI) should be considered as effective input for SR estimation. Finally, the measurement and analysis of the uncertainty based on the multi-objective shark algorithm-ANFIS were carried out. As a result, the proposed new hybrid model is highly suitable for the generation of accurate zone mapping for different renewable energy generation fields. In addition, the proposed hybrid model showed outstanding performance for the development of a forecasting model for the solar radiation value, which is essential for the decision-makers to draw a future plan for generating renewable energy based solar radiation.

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“…The number of input layers was seven variables (given by NCA feature selection). The selection of hidden neurons is the complicated part in ANN modelling, as it relates to the complexity of the system being modelled (Amirkhani, Nasirivatan, Kasaeian, & Hajinezhad, 2015;Quej, Almorox, Arnaldo, & Saito, 2017). By using the Equation [1] the initial number of neurons was calculated as 15 and a range of 15-50 neurons were successively trialled until the output converged to a minimum Mean Square Error (MSE).…”

Section: Ann Implementationmentioning

confidence: 99%