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

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  • Harish Kumar Ghritlahre

    (National Institute of Technology)

  • Radha Krishna Prasad

    (National Institute of Technology)

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.

Suggested Citation

  • Harish Kumar Ghritlahre & Radha Krishna Prasad, 2018. "Development of Optimal ANN Model to Estimate the Thermal Performance of Roughened Solar Air Heater Using Two different Learning Algorithms," Annals of Data Science, Springer, vol. 5(3), pages 453-467, September.
    • Handle: RePEc:spr:aodasc:v:5:y:2018:i:3:d:10.1007_s40745-018-0146-3
    DOI: 10.1007/s40745-018-0146-3
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    References listed on IDEAS

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    1. Patil, Anil Kumar, 2015. "Heat transfer mechanism and energy efficiency of artificially roughened solar air heaters—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 681-689.
    2. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    3. Jani, D.B. & Mishra, Manish & Sahoo, P.K., 2016. "Performance prediction of solid desiccant – Vapor compression hybrid air-conditioning system using artificial neural network," Energy, Elsevier, vol. 103(C), pages 618-629.
    4. Bhushan, Brij & Singh, Ranjit, 2010. "A review on methodology of artificial roughness used in duct of solar air heaters," Energy, Elsevier, vol. 35(1), pages 202-212.
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    Cited by:

    1. M. Sridharan, 2023. "Application of Generalized Regression Neural Network in Predicting the Performance of Solar Photovoltaic Thermal Water Collector," Annals of Data Science, Springer, vol. 10(1), pages 1-23, February.
    2. Harish Kumar Ghritlahre & Purvi Chandrakar & Ashfaque Ahmad, 2021. "A Comprehensive Review on Performance Prediction of Solar Air Heaters Using Artificial Neural Network," Annals of Data Science, Springer, vol. 8(3), pages 405-449, September.
    3. Manoj Verma & Harish Kumar Ghritlahre & Ghrithanchi Chandrakar, 2023. "Wind Speed Prediction of Central Region of Chhattisgarh (India) Using Artificial Neural Network and Multiple Linear Regression Technique: A Comparative Study," Annals of Data Science, Springer, vol. 10(4), pages 851-873, August.
    4. M. Sridharan, 2023. "Generalized Regression Neural Network Model Based Estimation of Global Solar Energy Using Meteorological Parameters," Annals of Data Science, Springer, vol. 10(4), pages 1107-1125, August.

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