Solar air heater (SAH) is an important device for solar energy
utilization which is used for space heating, crop drying, timber seasoning etc.
Its performance mainly depends on system parameters, operating parameters and meteorological parameters. Many researchers have been used these
parameters to predict the performance of SAH by analytical or conventional
approach and artificial neural network (ANN) technique, but performance
prediction of SAH by using relevant input parameters has not been done
so far. Therefore, relevant input parameters have been considered in this
study. Total ten parameters were used such as mass flow rate, ambient temperature, wind speed, relative humidity, fluid inlet temperature, fluid mean
temperature, plate temperature, wind direction, solar elevation and solar
intensity to find out the relevant parameters for ANN prediction. Seven
different neural models have been constructed using these parameters. In
each model 10 to 20 neurons have been selected to find out the optimal
model. The optimal neural models for ANN-I, ANN-II, ANN-III, ANN-IV,
ANN-V, ANN-VI and ANN-VII were obtained as 10-17-1, 8-14-1, 6-16-1, 5-
14-1, 4-17-1, 3-16-1 and 2-14-1, respectively. It has been found that ANN-II
model with 8-14-1 is the optimal model as compared to other neural models.
Values of the sum of squared errors, mean relative error, and coefficient of
determination were found to be 0.02138, 1.82% and 0.99387, respectively,
which shows that the ANN-II developed with mass flow rate, ambient temperature, inlet and mean temperature of air, plate temperature, wind speed
and direction, relative humidity, and relevant input parameters performed better. The above results show that these eight parameters are relevant for
prediction.
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