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Artificial Intelligence for the Prediction of the Thermal Performance of Evaporative Cooling Systems

Author

Listed:
  • Hafiz M. Asfahan

    (Department of Agricultural Engineering, Bahauddin Zakariya University, Bosan Road, Multan 60800, Pakistan
    These authors contributed equally to this work.)

  • Uzair Sajjad

    (Department of Mechanical Engineering, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu 300, Taiwan
    These authors contributed equally to this work.)

  • Muhammad Sultan

    (Department of Agricultural Engineering, Bahauddin Zakariya University, Bosan Road, Multan 60800, Pakistan
    These authors contributed equally to this work.)

  • Imtiyaz Hussain

    (Department of Power Mechanical Engineering, National Tsing Hua University, No. 101, Section 2, Guangfu Road, East District, Hsinchu 300, Taiwan)

  • Khalid Hamid

    (Department of Mechanical Engineering, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu 300, Taiwan)

  • Mubasher Ali

    (Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, China)

  • Chi-Chuan Wang

    (Department of Mechanical Engineering, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu 300, Taiwan)

  • Redmond R. Shamshiri

    (Department of Engineering for Crop Production, Leibniz Institute for Agricultural Engineering and Bioeconomy, 14469 Potsdam-Bornim, Germany)

  • Muhammad Usman Khan

    (Department of Energy Systems Engineering, Faculty of Agricultural Engineering and Technology, University of Agriculture, Faisalabad, Punjab 38040, Pakistan)

Abstract

The present study reports the development of a deep learning artificial intelligence (AI) model for predicting the thermal performance of evaporative cooling systems, which are widely used for thermal comfort in different applications. The existing, conventional methods for the analysis of evaporation-assisted cooling systems rely on experimental, mathematical, and empirical approaches in order to determine their thermal performance, which limits their applications in diverse and ambient spatiotemporal conditions. The objective of this research was to predict the thermal performance of three evaporation-assisted air-conditioning systems—direct, indirect, and Maisotsenko evaporative cooling systems—by using an AI approach. For this purpose, a deep learning algorithm was developed and lumped hyperparameters were initially chosen. A correlation analysis was performed prior to the development of the AI model in order to identify the input features that could be the most influential for the prediction efficiency. The deep learning algorithm was then optimized to increase the learning rate and predictive accuracy with respect to experimental data by tuning the hyperparameters, such as by manipulating the activation functions, the number of hidden layers, and the neurons in each layer by incorporating optimizers, including Adam and RMsprop. The results confirmed the applicability of the method with an overall value of R 2 = 0.987 between the input data and ground-truth data, showing that the most competent model could predict the designated output features ( T o u t d b , w o u t , and E o u t a i r ). The suggested method is straightforward and was found to be practical in the evaluation of the thermal performance of deployed air conditioning systems under different conditions. The results supported the hypothesis that the proposed deep learning AI algorithm has the potential to explore the feasibility of the three evaporative cooling systems in dynamic ambient conditions for various agricultural and livestock applications.

Suggested Citation

  • Hafiz M. Asfahan & Uzair Sajjad & Muhammad Sultan & Imtiyaz Hussain & Khalid Hamid & Mubasher Ali & Chi-Chuan Wang & Redmond R. Shamshiri & Muhammad Usman Khan, 2021. "Artificial Intelligence for the Prediction of the Thermal Performance of Evaporative Cooling Systems," Energies, MDPI, vol. 14(13), pages 1-20, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:3946-:d:586759
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    References listed on IDEAS

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