Development of Optimal ANN Model to Estimate the Thermal Performance of Roughened Solar Air Heater Using Two different Learning Algorithms

Harish Kumar Ghritlahre () and Radha Krishna Prasad ()
Additional contact information
Harish Kumar Ghritlahre: National Institute of Technology
Radha Krishna Prasad: National Institute of Technology

Annals of Data Science, 2018, vol. 5, issue 3, No 8, 453-467

Abstract: Abstract In the present study, artificial neural network (ANN) model has been developed with two different training algorithms to predict the thermal efficiency of wire rib roughened solar air heater. Total 50 sets of data have been taken from experiments with three different types of absorber plate. The experimental data and calculated values of collector efficiency were used to develop ANN model. Scaled conjugate gradient (SCG) and Levenberg–Marquardt (LM) learning algorithms were used. It has been found that TRAINLM with 6 neurons and TRAINSCG with 7 neurons is optimal model on the basis of statistical error analysis. The performance of both the models have been compared with actual data and found that TRAINLM performs better than TRAINSCG. The value of coefficient of determination $$(\hbox {R}^{2})$$ ( R 2 ) for LM-6 is 0.99882 which gives the satisfactory performance. Learning algorithm with LM based proposed MLP ANN model seems more reliable for predicting performance of solar air heater.

Keywords: Solar air heater; Artificial neural network; Energy analysis; Learning algorithm; Scaled conjugate gradient; Multi-layer perceptron (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://link.springer.com/10.1007/s40745-018-0146-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:aodasc:v:5:y:2018:i:3:d:10.1007_s40745-018-0146-3

Ordering information: This journal article can be ordered from
https://www.springer ... gement/journal/40745

DOI: 10.1007/s40745-018-0146-3

Access Statistics for this article

Annals of Data Science is currently edited by Yong Shi

More articles in Annals of Data Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().