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

  • Harish Kumar GhritlahreEmail author
  • Purvi Chandrakar
  • Ashfaque Ahmad


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.


Solar air heaters Porous bed Artificial roughness Artificial neural network Learning algorithm Multi-layer perceptron 

List of Symbols


Area of collector surface (m2)


Artificial neural networks


Input data




Specific heat (J/kg K)


Polak–Ribiére conjugate gradient


Coefficient of variance

\( \dot{E} \)

Energy rate (W)

\( {\dot{Ex}} \)

Exergy rate (W)

\( {\dot{Ex}}_{{_{dest} }} \)

Rate of irreversibility (W)


Genetic algorithm


Generalized regression neural network


Enthalpy (J/kg)


Solar intensity (W/m2)

\( \dot{IP} \)

Rate of improvement potential (W)




Input parameters

\( \dot{m}_{f} \)

Mass flow rate of air (kg/s)


Mean relative error


Mean square error


Mean absolute error


Multi-layered perceptron


Multivariable linear regression


Multi-layer feed forward neural network


Output parameters


Nonlinear autoregressive exogenous model


Nonlinear regression


One step secant


Fluid pressure (Pa)


Particle swarm optimization

\( \dot{Q}_{c} \)

Rate of incident energy on the collector area (W)

\( \dot{Q}_{u} \)

Rate of useful energy gained by air (W)


Root mean square error


Correlation coefficient


Radial basis function


Universal gas constant (J/kg K)


Coefficient of multiple determination


Specific entropy (J/kg K)


Solar air heater


Scaled conjugate gradient


Sum square error


Surface volume method


Temperature (K)


Number of training data sets




Actual value


Predicted value


Wavelet neural network

Greek Letters


Thermal efficiency


Exergy efficiency


Specific exergy (J/kg)

\( \rho_{a} \)

Density of air (kg/m3)

\( \beta \)

Ratio of orifice diameter to pipe diameter



Ambient air






Inlet air


Outlet air


Mean air









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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Harish Kumar Ghritlahre
    • 1
    Email author
  • Purvi Chandrakar
    • 1
  • Ashfaque Ahmad
    • 1
  1. 1.Department of Energy and Environmental EngineeringChhattisgarh Swami Vivekanand Technical UniversityBhilaiIndia

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