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%.

Issue Section:
Research Papers
Keywords:
solar, SFPCS, performance, GRNN, cognitive, prediction and accuracy, energy efficiency, energy systems, experimental/measurement techniques, heat pipes
Topics:
Artificial neural networks, Flat plates, Solar energy, Solar radiation, Thermal efficiency, Thermal energy, Fluids, Temperature, Design, Errors

References

1.
Sivakumar
,
P.
,
Christraj
,
W.
,
Sridharan
,
M.
, and
Jayamalathi
,
N.
,
2012
, “
Performance Comparison of Differently Configured Solar Water Heaters
,”
Eur. J. Sci. Res.
,
91
(
1
), pp.
23
–
31
.
2.
Sridharan
,
M.
,
2019
, “
Verification and Validation of Solar Photovoltaic Thermal Water Collectors Performance Using Fuzzy Logic
,”
J. Verif. Valid. Uncert.
,
12
(
3
), pp.
1
–
8
. 10.1115/1.4045895
3.
Kumar
,
S.
,
Singh
,
G.
,
Tiwari
,
G. N.
, and
Yadav
,
J. K.
,
2012
, “
Thermal Modelling of a Hybrid Photovoltaic Thermal Water Heater in Parallel Configuration
,”
Int. J. Sustain. Energy
, pp.
1
–
19
. 10.1080/14786451.2011.644627
4.
Sridharan
,
M.
,
Jayaprakash
,
G.
,
Chandrasekar
,
M.
,
Vigneshwar
,
P.
,
Paramaguru
,
S.
, and
Amarnath
,
K.
,
2018
, “
Prediction of Solar Photovoltaic/Thermal Collector Power Output Using Fuzzy Logic
,”
ASME J. Sol. Energy Eng.
,
140
(
6
), pp.
1
–
6
. 10.1115/1.4040757
Google Scholar
Crossref
Search ADS
 
5.
Ghritlahre
,
H. K.
, and
Prasad
,
R. K.
,
2018
, “
Application of ANN Technique to Predict the Performance of Solar Collector Systems—A Review
,”
Renew. Sustain. Energy Rev.
,
84
(
3
), pp.
75
–
88
. 10.1016/j.rser.2018.01.001
6.
Venkataraman
,
A. P.
,
Veerapathran
,
V.
, and
Girirajkumar Marimuthu
,
S.
,
2020
, “
Error Recursion Reduction Computational Technique Based Control System Design for a Multivariable Process
,”
Instrumentation Mesure Métrologie
,
19
(
2
), pp.
133
–
140
. 10.18280/i2m.190208
Google Scholar
Crossref
Search ADS
 
7.
Ghritlahre
,
H. K.
, and
Prasad
,
R. K.
,
2018
, “
Exergetic Performance Prediction of a Roughened Solar Air Heater Using Artificial Neural Network
,”
Stroj. Vestn.—J. Mech. Eng.
,
64
(
3
), pp.
195
–
206
. 10.5545/sv-jme.2017.4575
8.
Ramachandran
,
S.
,
Aravind
,
P.
, and
Rathna Prabha
,
S.
,
2014
, “
Model Identification and Validation for a Nonlinear Process Using Recurrent Neural Networks
,”
Int. J. Res. Electron. Comput. Eng.
,
2
(
4
), pp.
13
–
18
.
9.
Bendu
,
H.
,
Deepak
,
B. B. V. L.
, and
Murugan
,
S.
,
2016
, “
Application of GRNN for the Prediction of Performance and Exhaust Emissions in HCCI Engine Using Ethanol
,”
Energy Convers. Manage.
,
122
(
4
), pp.
165
–
173
. 10.1016/j.enconman.2016.05.061
10.
Bendu
,
H.
,
Deepak
,
B. B. V. L.
, and
Murugan
,
S.
,
2017
, “
Multi-Objective Optimization of Ethanol Fuelled HCCI Engine Performance Using Hybrid GRNN–PSO
,”
Appl. Energy
,
187
(
6
), pp.
601
–
611
. 10.1016/j.apenergy.2016.11.072
11.
Specht
,
D. F.
,
1991
, “
A General Regression Neural Network
,”
IEEE Trans. Neural Networks
,
2
(
6
), pp.
568
–
576
. 10.1109/72.97934
Google Scholar
Crossref
Search ADS
 
12.
Sridharan
,
M.
,
2020
, “
Application of Generalized Regression Neural Network in Predicting the Performance of Natural Convection Solar Dryer
,”
ASME J. Sol. Energy Eng.
,
142
(
3
), pp.
1
–
8
. 10.1115/1.4045384
Google Scholar
Crossref
Search ADS
 
13.
Marimuthu
,
M.
,
Geetha
,
P.
,
Deepiha
,
P.
, and
Sridharan
,
M.
,
2015
, “
MATLAB Simulation of Transparent Glass PV/T Hybrid Water Collectors
,”
2015 IEEE 9th International Conference on Intelligent Systems and Control (ISCO)
,
Coimbatore, India
, pp.
1
–
7
,
Jan. 9–10
.
Google Scholar
Crossref
Search ADS
 
14.
Sridharan
,
M.
,
2020
, “
Predicting Performance of Double-Pipe Parallel- and Counter-Flow Heat Exchanger Using Fuzzy Logic
,”
ASME J. Thermal Sci. Eng. Appl.
,
12
(
3
), pp.
1
–
11
. 10.1115/1.4044696
15.
Zalnezhad
,
E.
,
Sarhan
,
A. A. D. M.
, and
Hamdi
,
M.
,
2013
, “
Surface Hardness Prediction of CrN Thin Film Coating on AL7075-T6 Alloy Using Fuzzy Logic System
,”
Int. J. Precis. Eng. Manuf.
,
14
(
3
), pp.
467
–
473
. 10.1007/s12541-013-0063-5
Google Scholar
Crossref
Search ADS
 
16.
Sridharan
,
M.
,
2020
, “
Application of Generalized Regression Neural Network in Predicting the Performance of Solar Photovoltaic Thermal Water Collector
,”
Ann. Data Sci.
10.1007/s40745-020-00273-1
17.
Sridharan
,
M.
,
Devi
,
R.
,
Dharshini
,
C. S.
, and
Bhavadarani
,
M.
,
2019
, “
IoT Based Performance Monitoring and Control in Counter Flow Double Pipe Heat Exchanger
,”
Internet Things
,
5
(
1
), pp.
34
–
40
. 10.1016/j.iot.2018.11.002
Copyright © 2020 by ASME
You do not currently have access to this content.