IDEAS home Printed from https://ideas.repec.org/a/spr/aodasc/v5y2018i3d10.1007_s40745-018-0146-3.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40745-018-0146-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40745-018-0146-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jani, D.B. & Mishra, Manish & Sahoo, P.K., 2017. "Application of artificial neural network for predicting performance of solid desiccant cooling systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 352-366.
    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. Kumar, Anil & Kim, Man-Hoe, 2016. "Thermohydraulic performance of rectangular ducts with different multiple V-rib roughness shapes: A comprehensive review and comparative study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 635-652.
    4. Ghritlahre, Harish Kumar & Prasad, Radha Krishna, 2018. "Application of ANN technique to predict the performance of solar collector systems - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 84(C), pages 75-88.
    5. Jani, D.B. & Mishra, Manish & Sahoo, P.K., 2016. "Solid desiccant air conditioning – A state of the art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1451-1469.
    6. Mehleri, E.D. & Zervas, P.L. & Sarimveis, H. & Palyvos, J.A. & Markatos, N.C., 2010. "A new neural network model for evaluating the performance of various hourly slope irradiation models: Implementation for the region of Athens," Renewable Energy, Elsevier, vol. 35(7), pages 1357-1362.
    7. Akbilgic, Oguz & Zhu, Da & Gates, Ian D. & Bergerson, Joule A., 2015. "Prediction of steam-assisted gravity drainage steam to oil ratio from reservoir characteristics," Energy, Elsevier, vol. 93(P2), pages 1663-1670.
    8. Sözen, Adnan & Ali Akçayol, M., 2004. "Modelling (using artificial neural-networks) the performance parameters of a solar-driven ejector-absorption cycle," Applied Energy, Elsevier, vol. 79(3), pages 309-325, November.
    9. Elsisi, Mahmoud & Amer, Mohammed & Dababat, Alya’ & Su, Chun-Lien, 2023. "A comprehensive review of machine learning and IoT solutions for demand side energy management, conservation, and resilient operation," Energy, Elsevier, vol. 281(C).
    10. Leung, Philip C.M. & Lee, Eric W.M., 2013. "Estimation of electrical power consumption in subway station design by intelligent approach," Applied Energy, Elsevier, vol. 101(C), pages 634-643.
    11. Jorge E. De León-Ruiz & Ignacio Carvajal-Mariscal & Antonin Ponsich, 2019. "Feasibility Analysis and Performance Evaluation and Optimization of a DXSAHP Water Heater Based on the Thermal Capacity of the System: A Case Study," Energies, MDPI, vol. 12(20), pages 1-38, October.
    12. Selimefendigil, Fatih & Öztop, Hakan F., 2020. "Identification of pulsating flow effects with CNT nanoparticles on the performance enhancements of thermoelectric generator (TEG) module in renewable energy applications," Renewable Energy, Elsevier, vol. 162(C), pages 1076-1086.
    13. Jin, Dongxu & Zhang, Manman & Wang, Ping & Xu, Shasha, 2015. "Numerical investigation of heat transfer and fluid flow in a solar air heater duct with multi V-shaped ribs on the absorber plate," Energy, Elsevier, vol. 89(C), pages 178-190.
    14. Wu, X.N. & Ge, T.S. & Dai, Y.J. & Wang, R.Z., 2019. "Investigation on novel desiccant wheel using wood pulp fiber paper with high coating ratio as matrix," Energy, Elsevier, vol. 176(C), pages 493-504.
    15. Khan, Waqas & Walker, Shalika & Zeiler, Wim, 2022. "Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach," Energy, Elsevier, vol. 240(C).
    16. 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.
    17. Souliotis, M. & Kalogirou, S. & Tripanagnostopoulos, Y., 2009. "Modelling of an ICS solar water heater using artificial neural networks and TRNSYS," Renewable Energy, Elsevier, vol. 34(5), pages 1333-1339.
    18. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    19. Mellit, Adel & Kalogirou, Soteris A., 2011. "ANFIS-based modelling for photovoltaic power supply system: A case study," Renewable Energy, Elsevier, vol. 36(1), pages 250-258.
    20. Abu Bakar, Nur Najihah & Hassan, Mohammad Yusri & Abdullah, Hayati & Rahman, Hasimah Abdul & Abdullah, Md Pauzi & Hussin, Faridah & Bandi, Masilah, 2015. "Energy efficiency index as an indicator for measuring building energy performance: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 1-11.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:aodasc:v:5:y:2018:i:3:d:10.1007_s40745-018-0146-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.