Experimentally validated thermal modeling for temperature prediction of photovoltaic modules under variable environmental conditions


Keddouda A., Ihaddadene R., Boukhari A., Atia A., ARICI M., Lebbihiat N., ...More

Renewable Energy, vol.231, 2024 (SCI-Expanded, Scopus) identifier identifier

  • Publication Type: Article / Article
  • Volume: 231
  • Publication Date: 2024
  • Doi Number: 10.1016/j.renene.2024.120922
  • Journal Name: Renewable Energy
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Communication Abstracts, Compendex, Environment Index, Geobase, Greenfile, Index Islamicus, INSPEC, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, DIALNET, Civil Engineering Abstracts
  • Keywords: Ambient conditions, Heat loss coefficient, Prediction, PV module temperature, Thermal modeling
  • Kocaeli University Affiliated: Yes

Abstract

In this work, a detailed analysis and thermal modeling for temperature prediction of a stand-alone photovoltaic module is performed. The study aims to present precise estimation of module temperature, since it is an important parameter for power output calculation. Hence, the required data were collected via experiments. Accounting for all heat transfer mechanisms, and following model validation, a proposed algorithm was implemented to investigate heat transfer from the module to its surrounding and predict different layers’ temperature. Results indicate that accurate energy distribution and temperature prediction was achieved by the adopted thermal model, only about 16% of the received energy is converted to electrical power while the rest is released by heat. Moreover, the proposed simulation algorithm provided one of the best results in comparison to literature models, achieving an R2 of 0.963 and a MAE of 1.883, which is very close to the best overall model by King at R2=0.973 and MAE=1.663. Additionally, two new models for module temperature prediction were proposed. After testing on new data, the explicit model provided a reasonable first approximation attaining an adjusted R2 of 0.97 and a MSE of 3.505, and an accurate implicit model, achieving a MSE of only 1.268.