Application of Generalized Regression Neural Network in Predicting the Performance of Natural Convection Solar Dryer

2019 ◽  
Vol 142 (3) ◽  
Author(s):  
M. Sridharan
Keyword(s):  
Neural Network ◽  
Natural Convection ◽  
Network Architecture ◽  
Experimental Studies ◽  
Generalized Regression Neural Network ◽  
Atmospheric Conditions ◽  
Time Duration ◽  
Transient Nature ◽  
Solar Dryer ◽  
Generalized Regression

Abstract The performance evaluation of a natural convection solar dryer is a complex one because of the transient and non-linear nature of atmospheric conditions. In this comparative study, a smart neural network -based tool was developed for estimating the performance of such a transient nature solar dryer. For this purpose, a series of experimental studies are conducted through four successive days and compared with the generalized regression neural network (GRNN) modeling. GRNN architecture proposed in this study consists of three inputs (time duration, irradiance, and ambient temperature) and four outputs (drying chamber temperature, the mass of moisture removed, drying rate, and dryer efficiency). Such generalized regression neural network architecture was trained, tested, and validated with real-time experimental variable data sets. The results of the GRNN model are in good agreement with experimental results. The overall accuracy of the proposed GRNN model in predicting the performance is 96.29%.

scholarly journals Predicting the Performance of Passive Solar Distillation Using Generalized Regression Neural Network

2021 ◽  
Author(s):  
Siva Sankaran
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Performance Appraisal ◽  
Network Architecture ◽  
Performance Estimation ◽  
Generalized Regression Neural Network ◽  
Atmospheric Conditions ◽  
Solar Distillation ◽  
Generalized Regression ◽  
Passive Solar

Abstract In this study, the performance appraisal of the passive double slope solar distillation (PDSSD) was predicted using the generalized regression neural network (GRNN) model. The performance estimation of passive solar distillation is a complicated one because of unsteady and uncertain atmospheric conditions. For this purpose, a set of experiments has conducted for seven successive days, and results were compared with the GRNN model. The proposed GRNN consists of five inputs (solar irradiance, ambient temperature, basin temperature, surface water temperature, glass cover temperature) and two outputs (distillate yield and efficiency). Such network architecture was trained and validated with a set of experimental data values. The predicted results of the GRNN model follow a good trend with experimental data. The overall accuracy of the predicted GRNN is 99.58%.

Application of Generalized Regression Neural Network in Predicting the Thermal Performance of Solar Flat Plate Collector Systems

2020 ◽  
Vol 13 (2) ◽  
Author(s):  
M. Sridharan ◽  
Shribalaji Shenbagaraj
Keyword(s):  
Neural Network ◽  
Flat Plate ◽  
Thermal Performance ◽  
Experimental Studies ◽  
Generalized Regression Neural Network ◽  
Fluid Temperature ◽  
Transient Nature ◽  
Collector System ◽  
Generalized Regression ◽  
Flat Plate Collector

Abstract This study presents a smart neural network (NN) model for estimating the thermal performance of a transient nature solar flat plate collector system (SFPCS). For this purpose, a series of experimental studies are conducted through four successive days with three different arrangements of SFPCS (standalone, series, and parallel). Experimental results of such arrangements are then used for designing a generalized regression neural network (GRNN) model. The GRNN architecture proposed in this study consists of four inputs (mass flowrate, solar irradiance, fluid temperature difference, and collector area) and two dependent outputs (power output and efficiency of SFPCS). Such GRNN architecture is trained, tested, and validated with real-time experimental transient datasets for each arrangement individually. The results of the GRNN model are in good agreement with experimental datasets. The overall accuracy of the developed GRNN model in predicting the performance of standalone, series, and parallel connected SFPCS is 98%.

Risk of Voltage Collapse Using Multi-Layer Feed-Forward Neural Network and Generalized Regression Neural Network

2015 ◽  
Vol 793 ◽  
pp. 483-488
Author(s):  
N. Aminudin ◽  
Marayati Marsadek ◽  
N.M. Ramli ◽  
T.K.A. Rahman ◽  
N.M.M. Razali ◽  
...  
Keyword(s):  
Neural Network ◽  
System Analysis ◽  
Risk Index ◽  
Weather Condition ◽  
Generalized Regression Neural Network ◽  
Voltage Collapse ◽  
Ann Model ◽  
Feed Forward ◽  
Feed Forward Network ◽  
Generalized Regression

The computation of security risk index in identifying the system’s condition is one of the major concerns in power system analysis. Traditional method of this assessment is highly time consuming and infeasible for direct on-line implementation. Thus, this paper presents the application of Multi-Layer Feed Forward Network (MLFFN) to perform the prediction of voltage collapse risk index due to the line outage occurrence. The proposed ANN model consider load at the load buses as well as weather condition at the transmission lines as the input. In realizing the effectiveness of the proposed method, the results are compared with Generalized Regression Neural Network (GRNN) method. The results revealed that the MLFFN method shows a significant improvement over GRNN performance in terms of least error produced.

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