A fuzzy regression with support vector machine approach to the estimation of horizontal global solar radiation

Baser, Furkan; Demirhan, Haydar

doi:10.1016/j.energy.2017.02.008

Cited by 87 publications

(13 citation statements)

References 49 publications

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“…To improve the accuracy prediction by eliminating the limitations of ANN and FL, a combine technique of ANN and FL namely ANFIS is proposed. 64,65 In neuro-fuzzy, decided rules are trained to predict the accurate results. Basic architecture of neuro-fuzzy modular is presented in Figure 6.…”

Section: Adaptive Neuro-fuzzy Interface System (Anfis)mentioning

confidence: 99%

A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

2020

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The uncertainty associated with modeling and performance prediction of solar photovoltaic systems could be easily and efficiently solved by artificial intelligence techniques. During the past decade of 2009 to 2019, artificial neural network (ANN), fuzzy logic (FL), genetic algorithm (GA) and their hybrid models are found potential artificial intelligence tools for performance prediction and modeling of solar photovoltaic systems. In addition, during this decade there is no extensive review on applicability of ANN, FL, GA and their hybrid models for performance prediction and modeling of solar photovoltaic systems. Therefore, this article focuses on extensive review on design, modeling, maximum power point tracking, fault detection and output power/efficiency prediction of solar photovoltaic systems using artificial intelligence techniques of the ANN, FL, GA and their hybrid models. In addition, the selected articles on the solar radiation prediction using ANN, FL, GA and their hybrid models are also summarized. Total of 122 articles are reviewed and summarized in the present review for the period of 2009 to 2019 with 90 articles in the field of {ANN, FL, GA and their hybrid models} + solar photovoltaic systems and 32 articles in the field of {ANN, FL, GA and their hybrid models} + solar radiation. The review shows the suitability and reliability of ANN, FL, GA and hybrid models for accurate prediction of the solar radiation and the performance characteristics of solar photovoltaic systems. In addition, this review presents the guidance for the researchers and engineers in the field of solar photovoltaic systems to select the suitable prediction tool for enhancement of the performance characteristics of the solar photovoltaic systems and the utilization of the available solar radiation.

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Section: Adaptive Neuro-fuzzy Interface System (Anfis)mentioning

confidence: 99%

A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

2020

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“…Therefore, providing an intelligent fault-diagnosis method is of great significance for ensuring the safety of coal mine production. As one of the most intelligent and cutting-edge fields in the field of artificial intelligence, the application of SVM received increasing attention [ 12 , 13 , 14 ], reflecting in the aspects of regression estimation, pattern recognition, and fault diagnosis, such as the fault diagnosis of the vehicle suspensions, automatic detection of diabetic eye disease, and predictive control of the industrial process [ 15 , 16 , 17 ]. In the aspect of bearing fault diagnosis, the application of SVM has been reported in many literatures [ 18 , 19 , 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Application of ICEEMDAN Energy Entropy and AFSA-SVM for Fault Diagnosis of Hoist Sheave Bearing

Kou

Yang

et al. 2020

Entropy

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The mine hoist sheave bearing is a large heavy-duty bearing, located in a derrick of tens of meters. Aiming at the difficulty of sheave bearing fault diagnosis, a combined fault-diagnosis method based on the improved complete ensemble EMD (ICEEMDAN) energy entropy and support vector machine (SVM) optimized by artificial fish swarm algorithm (AFSA) was proposed. Different location of the bearing defect will result in different frequency components and different amplitude energy of the frequency. According to this feature, the position of the bearing defect can be determined by calculating the ICEEMDAN energy entropy of different vibration signals. In view of the difficulty in selecting the penalty factor and radial basis kernel parameter in the SVM model, the AFSA was used to optimize them. The experimental results show that the accuracy rate of the optimized fault-diagnosis model is improved by 10% and the diagnostic accuracy rate is 97.5%.

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“…For this reason, estimation models made with hourly data are less common since they contain more difficult and complex processes. After determining the input data according to the type of work that will be carried out, the SR values of the target area can be estimated by using one [14], multiple [15], or hybrid [16] ML algorithms. Different solutions were sought for Global Solar Radiation (GSR) estimation problems in developed models by making changes on the functional structure and architecture of one single ML algorithm by comparing multiple algorithms or by working two or more AI methods together.…”

Section: Introductionmentioning

confidence: 99%

Effective Estimation of Hourly Global Solar Radiation Using Machine Learning Algorithms

Guher

Taşdemir

Yanıktepe

2020

International Journal of Photoenergy

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The precise estimation of solar radiation is of great importance in solar energy applications with respect to installation and capacity. In estimate modelling on selected target locations, various computer-based and experimental methods and techniques are employed. In the present study, the Multilayer Feed-Forward Neural Network (MFFNN), K -Nearest Neighbors ( K -NN), a Library for Support Vector Machines (LibSVM), and M5 rules algorithms, which are among the Machine Learning (ML) algorithms, were used to estimate the hourly average solar radiation of two geographic locations on the same latitude. The input variables that had the most impact on solar radiation were identified and grouped as a result of 29 different applications that were developed by using 6 different feature selection methods with Waikato Environment for Knowledge Analysis (WEKA) software. Estimation models were developed by using the selected data groups and all input variables for each target location. The results show that the estimations developed with the feature selection method were more successful for target locations, and the radiation potentials were similar. The performance of the estimation models was evaluated by comparing each model with different statistical indicators and with previous studies. According to the RMSE, MAE, R 2 , and SMAPE statistical scales, the results of the most successful estimation models that were developed with MFFNN were 0.0508-0.0536, 0.0341-0.0352, 0.9488-0.9656, and 7.77%-7.79%, respectively.

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A fuzzy regression with support vector machine approach to the estimation of horizontal global solar radiation

Cited by 87 publications

References 49 publications

A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

Application of ICEEMDAN Energy Entropy and AFSA-SVM for Fault Diagnosis of Hoist Sheave Bearing

Effective Estimation of Hourly Global Solar Radiation Using Machine Learning Algorithms

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