Implementation of ANN technique for performance prediction of solar thermal systems: A Comprehensive Review

Ahmad, Ashfaque; Ghritlahre, Harish Kumar; Chandrakar, Purvi

doi:10.17737/tre.2020.6.1.00110

Cited by 14 publications

(7 citation statements)

References 68 publications

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“…RBF, FCM, and NARX, classified as three ANNs’ functions, were selected and used to develop a COVID-19 prediction model. These procedures are conformed as powerful ANNs’ functions through their mathematical ability for data analyses and predict results when they are involved in developing artificial intelligent models (Mirmozaffari 2019 ; Yahya and Seker 2019 ; Ahmad et al 2020 ).…”

Section: Methodsmentioning

confidence: 99%

COVID-19 prediction analysis using artificial intelligence procedures and GIS spatial analyst: a case study for Iraq

Yahya

Thannoun

2021

Appl Geomat

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The prediction of diseases caused by viral infections is a complex medical task where many real data that consists of different variables must be employed. As known, COVID-19 is the most dangerous disease worldwide; nowhere, an effective drug has been found yet. To limit its spread, it is essential to find a rational method that shows the spread of this virus by relying on many infected people’s data. A model consisting of three artificial neural networks’ (ANN) functions was developed to predict COVID-19 separation in Iraq based on real infection data supplied by the public health department at the Iraqi Ministry of Health. The performance efficiency of this model was evaluated, where its performance efficiency reached 81.6% when employed four statistical error criteria as mean absolute percentage error (MAPE), root mean square error (RMSE), coefficient of determination ( R 2 ), and Nash-Sutcliffe coefficient (NC). The severity of the virus’s spread across Iraq was assessed in a short term (in the next 6 months), where the results show that the spread severity will intensify in this short term by 17.1%, and the average death cases will increase by 8.3%. These results clarified by creating spatial distribution maps for virus spread are simulated by employing a Geographic Information System (GIS) environment to be used as a useful database for developing plans for combating viruses in Iraq.

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Section: Methodsmentioning

confidence: 99%

COVID-19 prediction analysis using artificial intelligence procedures and GIS spatial analyst: a case study for Iraq

Yahya

Thannoun

2021

Appl Geomat

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“…ANN have been used by a few previous researches to represent and forecast STS performance. The use of the ANN technique has been very common in the field of thermal engineering systems in the last two decades [11]- [18].…”

Section: Artificial Intelligence Techniques 31 Artificial Neural Networkmentioning

confidence: 99%

Implementation of artificial intelligence for prediction performance of solar thermal system

Sufian

Salim

Mohamad

et al. 2022

IJPEDS

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A related input parameter is used in this case study to forecast solar thermal systems (STS) capabilities and to compare which artificial neural network (ANN) algorithms and other artificial intelligence (AI) methods have the most reliable predictor for STS performance. In order to gauge the performance of the STS, this research aims to implement AI for predicting STS performance by comparing the ANN technique with other methods. Three different training algorithms which are Levenberg-Marquardt (LM), scaled conjugate gradient (SCG) and Bayesian regularization (BR) are considered in this research. This research will identify acceptable parameters and the best AI technique to use in predicting the STS performance. Previous research on STS demonstrates that the efficiency of STS has been estimated using different input parameters. The results show that the prediction of the LM training algorithm is the best for STS performance.

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“…Composed of few layers and neural processors, a multi-layer perceptron (MLP) [24] is known as a widely-used type of ANNs. These processors have been profitably applied for energy-related simulations [25][26][27]. An MLP maps the association of a dependent parameter with independent factors.…”

Section: Introductionmentioning

confidence: 99%

A TLBO-Tuned Neural Processor for Predicting Heating Load in Residential Buildings

Almutairi¹,

Algarni²,

Alqahtani³

et al. 2022

Preprint

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Recent studies have witnessed the remarkable merits of metaheuristic algorithms in optimization problems. Due to the significance of early analysis of thermal load in energy-efficient buildings, this work is carried out to introduce and compare four novel optimizer techniques of firefly algorithm (FA), optics inspired optimization (OIO), shuffled complex evolution (SCE), and teaching-learning-based optimization (TLBO) for accurate prediction of heating load (HL). The models are applied to a multilayer perceptron (MLP) neural network to surmount its computational shortcomings. The models are fed by a literature-based dataset obtained for residential buildings. The results revealed that all used models are capable of properly analyzing and predicting the HL pattern. A comparison between them, however, showed that the TLBO-MLP with the co-efficients of determination 0.9610 vs. 0.9438, 0.9373, and 0.9556 (respectively for FA-MLP, OIO-MLP, and SCE-MLP) and also root mean square error of 2.1103 vs. 2.5456, 2.7099, and 2.2774 presents the most reliable approximation of the HL. It also surpassed several methods used in previous studies. Thus, the developed TLBO-MLP can a beneficial model for subsequent practical applications.

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Implementation of ANN technique for performance prediction of solar thermal systems: A Comprehensive Review

Abstract: Peer-Reviewed Review Article futureenergysp.com/index.php/tre

Cited by 14 publications

References 68 publications

COVID-19 prediction analysis using artificial intelligence procedures and GIS spatial analyst: a case study for Iraq

COVID-19 prediction analysis using artificial intelligence procedures and GIS spatial analyst: a case study for Iraq

Implementation of artificial intelligence for prediction performance of solar thermal system

A TLBO-Tuned Neural Processor for Predicting Heating Load in Residential Buildings

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