Comparison of prediction methods of PV/T nanofluid and nano-PCM system using a measured dataset and artificial neural network. (1st March 2018)

• Record Type: Journal Article
• Authors: Al-Waeli, Ali H.A.; Sopian, K.; Kazem, Hussein A.; Yousif, Jabar H.; Chaichan, Miqdam T.; Ibrahim, Adnan; Mat, Sohif; Ruslan, Mohd Hafidz
• Is Part Of: Solar energy. Volume 162(2018)
• Page Start: 378
• Page End: 396
• Publication Date: 2018-03-01
• Subjects: Hybrid PV/T collectors -- Nanofluid -- Nano-PCM -- Artificial neural network; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2018.01.026
• Languages: English
• ISSNs: 0038-092X

Abstract

Graphical abstract: Highlights: The effects of nanofluid and nano-PCM cooling method was proposed and investigated. An experimental analysis on hybrid PV/T is carried out. The artificial neural networks is trained by the experimental data. The electrical efficiency of PV/T compared with PV, is 13.32% and 8.07%, respectively. 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.