TY  - JOUR
TI  - Predicting The Performance of Solar Collector Using Advanced Clustering with Artificial Neutral Networking
AU  - Richika Kumari
AU  -  Harsh Mathur
JO  - International Journal of Scientific Research in Computer Science, Engineering and Information Technology
PB  - Technoscience Academy
DA  - 2022/11/30
PY  - 2022
DO  - https://doi.org/10.32628/IJSRCSEIT
UR  - https://ijsrcseit.com/CSEIT20124
VL  - 8
IS  - 6
SP  - 667	
EP  - 676
AB  - In the present study three different types of neural models: multi-layer perceptron (MLP), has been used to predict the exergetic efficiency of roughened solar air heater. The operation of a flat-plate solar collector using water as a working fluid flows (water, i.e. 1 L/min) has been modelled using the artificial neural networks (ANNs) of computational intelligence technique. The ANNs model has been built at the entrance to predict the outlet temperature in the flat-plate solar collector using measured data of solar irradiance, ambient temperature, inlet temperature and working fluid flowA novel all-glass straight-through tube solar collector is employed as reference solar technology. In the present approach, experimental collector performance data was first collected during different weather conditions (sunny, cloudy, rainy days) subject to a clustering analysis to screen out outlier samples. The data was then used to train and verify the neural network models. For the ANN, the Back Propagation (BP) and Convolutional Neural Network (CNN) models were used. For predicting the performance (thermal efficiency) of the solar collector, solar radiation intensity, ambient temperature, wind speed, fluid flow rate, and fluid inlet temperature were used as the input parameters in the model. The prediction accuracy of the neural network models after full-data-screening were superior to that of the pre-screening and partial-screening models. The CNN model provided somewhat better efficiency predictions than the BP model. The R2, RMSE and MAE of the CNN model prediction in sunny conditions with full-screening was 0.9693, 0.0039 and 0.0030, respectively. The average MAPE of the BP and CNN models for all three weather types dropped by 30.7% and 13.8%, respectively, when applying data pre-screening and partial-screening only. The accuracy of the ANN collector prediction model can thus be improved through data clustering, which provides an effective method for performance prediction of solar collectors.