Comparison of prediction methods of PV/T nanofluid and nano-PCM system using a measured dataset and artificial neural network

Ali H.A. Al-Waeli, Kamaruzzaman Sopian, Hussein A. Kazem, Jabar H. Yousif, Miqdam T. Chaichan, Adnan Ibrahim, Sohif Mat, Mohd Hafidz Ruslan

Research output: Contribution to journalArticle

28 Citations (Scopus)

Abstract

In this paper, a Photovoltaic/Thermal (PV/T) system was proposed, built and tested. Three various types of cooling were proposed: tank filled with water and water flows through the cooling pipes, tank filled with PCM and water flows through the cooling pipes, and tank filled with PCM/nano-SiC and nanofluid (water-SiC) flows through the cooling pipes. The three proposed systems results were compared with conventional PV. According to the results, it was found that nano-PCM and nanofluid improved the electrical current from 3.69 A to 4.04, and the electrical efficiency from 8.07% to 13.32%, compared with conventional PV. In addition, three Artificial Neural Network (ANN), MLP, SOFM and SVM methods were implemented using the experimental results. The results indicate that the output of the network is in good agreement with the experimental results and published works.

Original languageEnglish
Pages (from-to)378-396
Number of pages19
JournalSolar Energy
Volume162
DOIs
Publication statusPublished - 1 Mar 2018

Fingerprint

Pulse code modulation
Cooling
Neural networks
Water
Pipe
Hot Temperature

Keywords

  • Artificial neural network
  • Hybrid PV/T collectors
  • Nano-PCM
  • Nanofluid

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Materials Science(all)

Cite this

Comparison of prediction methods of PV/T nanofluid and nano-PCM system using a measured dataset and artificial neural network. / Al-Waeli, Ali H.A.; Sopian, Kamaruzzaman; Kazem, Hussein A.; Yousif, Jabar H.; Chaichan, Miqdam T.; Ibrahim, Adnan; Mat, Sohif; Ruslan, Mohd Hafidz.

In: Solar Energy, Vol. 162, 01.03.2018, p. 378-396.

Research output: Contribution to journalArticle

@article{582cf65eec5f4d0b8153deb43a050922,
title = "Comparison of prediction methods of PV/T nanofluid and nano-PCM system using a measured dataset and artificial neural network",
abstract = "In this paper, a Photovoltaic/Thermal (PV/T) system was proposed, built and tested. Three various types of cooling were proposed: tank filled with water and water flows through the cooling pipes, tank filled with PCM and water flows through the cooling pipes, and tank filled with PCM/nano-SiC and nanofluid (water-SiC) flows through the cooling pipes. The three proposed systems results were compared with conventional PV. According to the results, it was found that nano-PCM and nanofluid improved the electrical current from 3.69 A to 4.04, and the electrical efficiency from 8.07{\%} to 13.32{\%}, compared with conventional PV. In addition, three Artificial Neural Network (ANN), MLP, SOFM and SVM methods were implemented using the experimental results. The results indicate that the output of the network is in good agreement with the experimental results and published works.",
keywords = "Artificial neural network, Hybrid PV/T collectors, Nano-PCM, Nanofluid",
author = "Al-Waeli, {Ali H.A.} and Kamaruzzaman Sopian and Kazem, {Hussein A.} and Yousif, {Jabar H.} and Chaichan, {Miqdam T.} and Adnan Ibrahim and Sohif Mat and Ruslan, {Mohd Hafidz}",
year = "2018",
month = "3",
day = "1",
doi = "10.1016/j.solener.2018.01.026",
language = "English",
volume = "162",
pages = "378--396",
journal = "Solar Energy",
issn = "0038-092X",
publisher = "Elsevier Limited",

}

TY - JOUR

T1 - Comparison of prediction methods of PV/T nanofluid and nano-PCM system using a measured dataset and artificial neural network

AU - Al-Waeli, Ali H.A.

AU - Sopian, Kamaruzzaman

AU - Kazem, Hussein A.

AU - Yousif, Jabar H.

AU - Chaichan, Miqdam T.

AU - Ibrahim, Adnan

AU - Mat, Sohif

AU - Ruslan, Mohd Hafidz

PY - 2018/3/1

Y1 - 2018/3/1

N2 - In this paper, a Photovoltaic/Thermal (PV/T) system was proposed, built and tested. Three various types of cooling were proposed: tank filled with water and water flows through the cooling pipes, tank filled with PCM and water flows through the cooling pipes, and tank filled with PCM/nano-SiC and nanofluid (water-SiC) flows through the cooling pipes. The three proposed systems results were compared with conventional PV. According to the results, it was found that nano-PCM and nanofluid improved the electrical current from 3.69 A to 4.04, and the electrical efficiency from 8.07% to 13.32%, compared with conventional PV. In addition, three Artificial Neural Network (ANN), MLP, SOFM and SVM methods were implemented using the experimental results. The results indicate that the output of the network is in good agreement with the experimental results and published works.

AB - In this paper, a Photovoltaic/Thermal (PV/T) system was proposed, built and tested. Three various types of cooling were proposed: tank filled with water and water flows through the cooling pipes, tank filled with PCM and water flows through the cooling pipes, and tank filled with PCM/nano-SiC and nanofluid (water-SiC) flows through the cooling pipes. The three proposed systems results were compared with conventional PV. According to the results, it was found that nano-PCM and nanofluid improved the electrical current from 3.69 A to 4.04, and the electrical efficiency from 8.07% to 13.32%, compared with conventional PV. In addition, three Artificial Neural Network (ANN), MLP, SOFM and SVM methods were implemented using the experimental results. The results indicate that the output of the network is in good agreement with the experimental results and published works.

KW - Artificial neural network

KW - Hybrid PV/T collectors

KW - Nano-PCM

KW - Nanofluid

UR - http://www.scopus.com/inward/record.url?scp=85041411078&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85041411078&partnerID=8YFLogxK

U2 - 10.1016/j.solener.2018.01.026

DO - 10.1016/j.solener.2018.01.026

M3 - Article

VL - 162

SP - 378

EP - 396

JO - Solar Energy

JF - Solar Energy

SN - 0038-092X

ER -