Artificial neural network modeling and analysis of photovoltaic/thermal system based on the experimental study

Ali H.A. Al-Waeli, Kamaruzzaman Sopian, Jabar H. Yousif, Hussein A. Kazem, John Boland, Miqdam T. Chaichan

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

A Photovoltaic/Thermal (PV/T) system combines PV and thermal collector, which is considered promising technology especially for building integrated PV/T system. The PV/T cooling systems using water, water-PCM and nanofluid/nano-PCM moves through the cooling pipes were investigated, in this study. However, this paper focuses on testing different PV/T systems (conventional PV, water-based PVT, water-nanofluid PVT, and nanofluid/nano-PCM) under the same conditions and environment using one artificial neural network (ANN) based Multi-Layer Perceptron (MLP) system. Also, investigate the differences in the efficiency of these systems on both thermal and electrical when using only one simulation system (MLP). The proposed ANN approach proved that using of nanofluid/nano-PCM was enhanced the electrical efficiency from 8.07% to 13.32% and its thermal efficiency reached 72%. Also, the voltage was improved significantly. Many measurement methods were used for validating the results of the proposed ANN model like the Mean Absolute Error (MAE), Mean Square Error (MSE), Correlation (R), and coefficient of determination (R 2 ). The proposed ANN model achieved a final MSE of 0.0229 in the training phase and 0.0282 in the cross-validation phase. The sensitivity analysis showed that the influence of solar irradiation and Amb-temp almost has a constant effect on electrical efficiency. However, the Ambient temperature had a significant impact on thermal efficiency. The results of the network were consistent with the experimental results of the current study and published works.

Original languageEnglish
Pages (from-to)368-379
Number of pages12
JournalEnergy Conversion and Management
Volume186
DOIs
Publication statusPublished - 15 Apr 2019

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Neural networks
Pulse code modulation
Multilayer neural networks
Mean square error
Water cooling systems
Water
Hot Temperature
Sensitivity analysis
Pipe
Irradiation
Cooling
Testing
Electric potential
Temperature

Keywords

  • Artificial neural network
  • Hybrid PV/T system
  • Nano-PCM
  • Nanofluid
  • Simulated multi-layer perceptron

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Nuclear Energy and Engineering
  • Fuel Technology
  • Energy Engineering and Power Technology

Cite this

Artificial neural network modeling and analysis of photovoltaic/thermal system based on the experimental study. / Al-Waeli, Ali H.A.; Sopian, Kamaruzzaman; Yousif, Jabar H.; Kazem, Hussein A.; Boland, John; Chaichan, Miqdam T.

In: Energy Conversion and Management, Vol. 186, 15.04.2019, p. 368-379.

Research output: Contribution to journalArticle

Al-Waeli, Ali H.A. ; Sopian, Kamaruzzaman ; Yousif, Jabar H. ; Kazem, Hussein A. ; Boland, John ; Chaichan, Miqdam T. / Artificial neural network modeling and analysis of photovoltaic/thermal system based on the experimental study. In: Energy Conversion and Management. 2019 ; Vol. 186. pp. 368-379.
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