Application of ANN technique to predict the performance of solar collector systems - A review

Ghritlahre, Harish Kumar; Prasad, Radha Krishna

doi:10.1016/j.rser.2018.01.001

Cited by 206 publications

(79 citation statements)

References 71 publications

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“…[34][35][36] For this purpose, energy systems are modeled and simulated through optimization methods such as an artificial neural network (ANN). [46][47][48] Kalani et al 49 utilized artificial neural network (ANN) and particle swarm optimization (PSO) technique to propose a model for estimating the performance of a PV/T solar collector. [46][47][48] Kalani et al 49 utilized artificial neural network (ANN) and particle swarm optimization (PSO) technique to propose a model for estimating the performance of a PV/T solar collector.…”

Section: Sadeghzadeh Et Almentioning

confidence: 99%

“…[37][38][39][40][41][42][43][44][45] Several studies have been done which employed smart techniques to model the efficiency of PV/T collectors. [46][47][48] Kalani et al 49 utilized artificial neural network (ANN) and particle swarm optimization (PSO) technique to propose a model for estimating the performance of a PV/T solar collector. The working fluid was ZnO/water nanofluid, and 130 experimental data sets were extracted through experiments to be used in the modeling procedure.…”

Section: Sadeghzadeh Et Almentioning

confidence: 99%

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Smart modeling by using artificial intelligent techniques on thermal performance of flat‐plate solar collector using nanofluid

Sadeghzadeh

Ahmadi

Kahani

et al. 2019

Energy Science & Engineering

112

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In the current study, Multilayer Perceptron Artificial Neural Network (MLP‐ANN) mode, Radial Basis Function Artificial Neural Network (RBF‐ANN), and Elman Back Propagation Neural Network (Elamn BP‐ANN) are developed to predict the thermal efficiency of a flat‐plate solar collector. TiO2 (20 nm)/water nanofluids are prepared using two‐step method and used in the designed solar system. All experiments are done in Mashhad city, Iran (Longitude/Latitude: 36.2605°N, 59.6168°E), according to EUROPEAN STANDARD EN 12975‐2 as a quasi‐dynamic test (QDT) method, and the solar collector is exposed to the south with the tilt angle of 55°. Three levels of inlet temperature (ambient air temperature, 52 and 74°C), 3 levels of volumetric flow rate (36, 72, and 108 L/(m2.h)), and 4 levels of nanofluid concentrations (0, 0.1, 0.2, and 0.3 wt.%) are considered as the input data, and the thermal efficiency of the solar system is calculated. According to the output results of developed models, the best prediction of thermal performance is obtained by MLP‐ANN model, although other generated models are also able to predict the efficiency of the solar collector with appropriated accuracy.

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Section: Sadeghzadeh Et Almentioning

confidence: 99%

Section: Sadeghzadeh Et Almentioning

confidence: 99%

Smart modeling by using artificial intelligent techniques on thermal performance of flat‐plate solar collector using nanofluid

Sadeghzadeh

Ahmadi

Kahani

et al. 2019

Energy Science & Engineering

112

View full text Add to dashboard Cite

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“…For better validation and to solve complex interdependency, stochastic tools like Artificial Neural Network (ANN) were used. These mathematical models were used in many fields for modelling, prediction, optimization, validation, and data mining …”

Section: Introductionmentioning

confidence: 99%

“…These mathematical models were used in many fields for modelling, prediction, optimization, validation, and data mining. 27,28 Here, we have demonstrated an in silico approach for screening functional SNPs from LEPR clinical variants from the intact group of missense mutations and identified novel variants. The identified deleterious SNPs were further validated using ANN using backpropagation neural network algorithm (BPNN).…”

Section: Introductionmentioning

confidence: 99%

Computational and artificial neural network based study of functional SNPs of human LEPR protein associated with reproductive function

Muruganandhan

Manian

2019

J of Cellular Biochemistry

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Genetic polymorphisms are mostly associated with inherited diseases, detecting and analyzing the biological significance of functional single‐nucleotide polymorphisms (SNPs) using wet laboratory experiments is an arduous task hence the computational analysis of putative SNPs is essential before conducting a study on a large population. SNP in the leptin receptor (LEPR) could result in the retention of intracellular signalling due to the structural and functional instability of the receptor causing abnormal reproductive function in human. In this first comprehensive computational analysis of LEPR gene mutation, we have identified and analyzed the functional consequence and structural significance of the SNPs in LEPR using recently developed several computational algorithms. Thirteen deleterious mutations such as W13C, S93G, I232R, Q307H, Y354C, E497A, Q571H, R612H, K656N, T690A, T699M V741M, and L760R were identified in the LEPR gene coding region. Backpropagation algorithm has been developed to forestall the deleterious nature of SNP and to validate the outcome of the tested computational tools. From ConSurf prediction three SNPs (Q571H, R612H, and T699M) were highly conserved on LEPR protein and the most deleterious variant R612H had one hydrogen bond abolished and severely reduced protein stability. Molecular docking suggested that the mutant (R612H) LEPR had lowest binding energy than native LEPR with the ligand molecule. Thus the energetically destructive changeover of ARG to HIS in R612H could possibly affect the LEPR protein structural stability and functional constancy due to interruption in the amino acid interactions and could result in reproductive disorders in human and increases the complication in obstetric and pregnancy outcome.

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“…The results showed that the BNN model outperforms other benchmark models such as ARIMA, ANN, and ANFIS. However, the practicability of these proposed ANN models is affected due to several weaknesses, such as timeconsuming, slow convergence velocity, trapping into local optimal solution easily [8][9][10][11][12], and challenging to determine suitable network structure [13].…”

Section: Introductionmentioning

confidence: 99%

Using Market Sentiment Analysis and Genetic Algorithm-Based Least Squares Support Vector Regression to Predict Gold Prices

Yuan¹,

Lee²,

Chiu³

2020

IJCIS

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Gold price prediction has long been a crucial and challenging research topic for gold investors. In conventional models, most scholars have used the historical gold price or economic indicators to forecast gold prices. The gold prices depend mainly on confidence in the current market. To reduce the time delay of economic indicators in this study, the daily online global gold news undergoes a text mining approach. An opinion score is generated by ascertaining the opinion polarity and words in the daily gold news. The opinion score represents the current market trends and used as an input predictor in the forecasting model. Subsequently, the least square support vector regression (LSSVR) that is optimized by the genetic algorithm (GA) is employed to train and predict the future gold price. The mean absolute percentage error (MAPE) is adopted to evaluate the model performance. This study is the first to use the opinion score through text mining as an input predictor to GA-LSSVR in forecasting gold prices. The experiment results demonstrate that the input predictor, opinion score, can improve the predicting ability of GA-LSSVR model in terms of MAPE.

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Application of ANN technique to predict the performance of solar collector systems - A review

Cited by 206 publications

References 71 publications

Smart modeling by using artificial intelligent techniques on thermal performance of flat‐plate solar collector using nanofluid

Smart modeling by using artificial intelligent techniques on thermal performance of flat‐plate solar collector using nanofluid

Computational and artificial neural network based study of functional SNPs of human LEPR protein associated with reproductive function

Using Market Sentiment Analysis and Genetic Algorithm-Based Least Squares Support Vector Regression to Predict Gold Prices

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