ANFIS-Based Modeling for Photovoltaic Characteristics Estimation

Bi, Ziqiang; Ma, Jing; Pan, Xinyu; Wang, Jian; Shi, Yu

doi:10.3390/sym8090096

Cited by 9 publications

(7 citation statements)

References 22 publications

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“…The artificial neural network was applied to civil engineering as early as 1989 [21], and then began to be widely welcomed in the fields of civil engineering, structural engineering, geotechnical environmental engineering, water resources engineering, and engineering geology. It is often used to solve problems such as concrete expansion prediction [22], saline soil frost heave prediction [23], soft rock strength prediction [24], composite material, and plastic hardening constitutive model identification, and so on [25][26][27]. The back propagation neural network (BPNN) is a sort of ANN which is widely applied at present.…”

Section: Introductionmentioning

confidence: 99%

Using ANFIS and BPNN Methods to Predict the Unfrozen Water Content of Saline Soil in Western Jilin, China

et al. 2018

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: Saline soil in seasonally frozen soil areas has caused tremendous damage to engineering and the ecological environment. The unfrozen water is the main factor affecting the properties of saline soil in seasonally frozen soil area and therefore needs to be studied. However, due to the high cost of laboratory measurement of the unfrozen water content, this study focuses on using an adaptive network fuzzy inference system (ANFIS) and a back propagation neural network (BPNN) to predict the unfrozen water content of saline soil in the Zhenlai area, Western Jilin. The data for the constructed model is obtained by nuclear magnetic resonance (NMR) testing. The initial water content (W0), salt content (S), and temperature (T) are used as input parameters for predicting the unfrozen water content (Wu). The results of the ANFIS and BPNN models are compared. The results show that although both methods are suitable for predicting the unfrozen water content of saline soil in the Zhenlai area, western Jilin, the prediction accuracy of the ANFIS model is higher.

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Section: Introductionmentioning

confidence: 99%

Using ANFIS and BPNN Methods to Predict the Unfrozen Water Content of Saline Soil in Western Jilin, China

et al. 2018

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“…A comparative study showed that the ANN-models performed better than polynomial regression, multiple linear regression, analytical five-parameter single-diode models. In Mellit and Kalogirou (2011) and Bi et al (2016), the authors used adaptive neuro-fuzzy inference scheme (ANFIS) in an expert configuration PV power supply system. The results showed that the ANFIS-based modeling method gave a good prediction accuracy of 98% and performed better than the ANN counterpart.…”

Section: Introductionmentioning

confidence: 99%

Electrical Characteristics Estimation of Photovoltaic Modules via Cuckoo Search—Relevant Vector Machine Probabilistic Model

Ban

Pan

2021

Front. Energy Res.

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This work presents an optimized probabilistic modeling methodology that facilitates the modeling of photovoltaic (PV) modules with measured data over a range of environmental conditions. The method applies cuckoo search to optimize kernel parameters, followed by electrical characteristics estimation via relevance vector machine. Unlike analytical modeling techniques, the proposed cuckoo search-relevance vector machine (CS-RVM) takes advantages of no required knowledge of internal PV parameters, more accurate estimation capability and less computational effort. A comparative study has been done among the electrical characteristics predicted by back-propagation neural network (BPNN), radial basis function neural network (RBFNN), support vector machine (SVM), Villalva's model, relevance vector machine (RVM), and the CS-RVM. Experimental results show that the proposed CS-RVM provides the best prediction in most scenarios.

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“…In this paper, four statistical factors were introduced to assess the predictive power of the two models. The four evaluation factors are the coefficient of determination (R 2 ), the mean squared error (MSE), the mean absolute percent error (MAPE) and the mean absolute error (MAE) [31,43]. These factors can be calculated using Equations ( 18)- (21).…”

Section: Models Comparison Methodsmentioning

confidence: 99%

“…It applies the back-gradient method and the least squares algorithm, and uses the "If-Then" rule for management and constraints. In addition, with the help of the membership function, it completes nonlinear mapping of the data from the input to the output [30,31]. The structure of the ANFIS model is shown in Figure 3.…”

Section: The Adaptive Network-based Fuzzy Inference Systemmentioning

confidence: 99%

Study on an AHP-Entropy-ANFIS Model for the Prediction of the Unfrozen Water Content of Sodium-Bicarbonate-Type Salinization Frozen Soil

et al. 2020

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The development of agriculture and ecology, and the construction of water conservancy facilities are seriously hindered by the salinization of seasonal frozen soil. Unfrozen water exists in the freezing and thawing of frozen soil. This unfrozen water is the core and foundation for studying the process of seasonal frozen soil salinization. However, it is difficult to obtain the unfrozen water content (UW) in routine experiments, and it shows nonlinear characteristics under the action of the main factors contained: salt content, water content, and temperature. In this paper, a new model is proposed to predict the UW of saline soil based on the combined weighting method and the adaptive neuro-fuzzy inference system (ANFIS). Firstly, the distance function was used to combine the analytic hierarchy process (AHP) with the entropy weight method (the combined weighting method) to determine the importance of the influencing factors (temperature, initial water content, and salt content) on UW. On this basis, the AHP, entropy weight method, and adaptive neuro-fuzzy inference system (AHP-entropy-ANFIS) ensemble model was established. Secondly, the five-fold cross-validation method and statistical factors (coefficient of determination, mean squared error, mean absolute percent error, and mean absolute error) were applied to evaluate and compare the AHP-entropy-ANFIS ensemble model, the ANFIS model, the support vector machine (SVM) model, and the AHP, entropy weight method, and support vector machine (AHP-entropy-SVM) ensemble model. In addition, the prediction values of the four models and the experimental values were also compared. The results show that the AHP-entropy-ANFIS model had the strongest prediction capability and the best stability, and so is more suitable for predicting the UW of saline soil. This study provides useful guidance for preventing and mitigating salinization hazards in seasonally frozen areas.

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ANFIS-Based Modeling for Photovoltaic Characteristics Estimation

Cited by 9 publications

References 22 publications

Using ANFIS and BPNN Methods to Predict the Unfrozen Water Content of Saline Soil in Western Jilin, China

Using ANFIS and BPNN Methods to Predict the Unfrozen Water Content of Saline Soil in Western Jilin, China

Electrical Characteristics Estimation of Photovoltaic Modules via Cuckoo Search—Relevant Vector Machine Probabilistic Model

Study on an AHP-Entropy-ANFIS Model for the Prediction of the Unfrozen Water Content of Sodium-Bicarbonate-Type Salinization Frozen Soil

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