A Comprehensive Review on Performance Prediction of Solar Air Heaters Using Artificial Neural Network

Harish Kumar Ghritlahre (), Purvi Chandrakar and Ashfaque Ahmad
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Harish Kumar Ghritlahre: Chhattisgarh Swami Vivekanand Technical University
Purvi Chandrakar: Chhattisgarh Swami Vivekanand Technical University
Ashfaque Ahmad: Chhattisgarh Swami Vivekanand Technical University

Annals of Data Science, 2021, vol. 8, issue 3, No 1, 405-449

Abstract: Abstract Solar air heater (SAH) is a most commonly used solar energy utilization system, which collects solar radiation on absorber plate and transmits absorbed thermal energy to the flowing air. Many techniques were used by various researchers for increasing the performance of SAHs by experimental examination, but analytical and experimental studies takes more time and are very costly. To avoid these types of problems soft computing techniques are used, in which artificial neural network (ANN) technique plays an important role to predict and optimize the performances of SAHs. This technique is very popular due to its fast computing speed and ability to solve complicated problems accurately which is not solved by other conventional approaches. For solving any problem programming code is not required which is the main advantage of this technique. The main purpose of present work is to review the work related to applications of neural model for performance prediction of SAHs and find out the research gap for future investigations. Various research works shown in this paper concluded that ANN is very efficient technique for performance prediction of SAHs.

Keywords: Solar air heaters; Porous bed; Artificial roughness; Artificial neural network; Learning algorithm; Multi-layer perceptron (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s40745-019-00236-1

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