Determination of the PV Module Surface Temperature Based on Neural Network Using Solar Radiation and Surface Temperature: Recent Development

  • Aiat Hegazy Department Solar Energy, National Research Centre, El Buhouth St., Dokki, Giza, Egypt.
  • E. T. El Shenawy Department Solar Energy, National Research Centre, El Buhouth St., Dokki, Giza, Egypt.
  • M. A. Ibrahim Department Solar Energy, National Research Centre, El Buhouth St., Dokki, Giza, Egypt.
Keywords: Photovoltaic energy, neural networks, temperature detection, training process

Abstract

Different attempts have been carried out to determine the PV module surface temperatures using mathematical models of the PV module, empirical formula and by neural networks. Neural network (NN) doesnt require any analysis of the system or scientific details; it only needs data from the system for training purposes. The present research describes the estimation of the PV module surface temperature using NN based on measured ambient temperatures and incident solar radiation. The NN is composed of input layer with two inputs (solar radiation and ambient temperature), hidden layer that has eight neurons and output layer to estimate the PV module surface temperature. Error back propagation algorithm was used to train the NN. The result showed that, the estimation accuracy of the PV module surface temperatures by the NN reached more than 96% of the measured values.

Published
2020-06-03
How to Cite
Hegazy, A., Shenawy, E. T. E., & Ibrahim, M. A. (2020). Determination of the PV Module Surface Temperature Based on Neural Network Using Solar Radiation and Surface Temperature: Recent Development. Recent Advances in Science and Technology Research Vol. 1, 141-151. Retrieved from https://stm1.bookpi.org/index.php/rastr-v1/article/view/1400
Issue
Recent Advances in Science and Technology Research Vol. 1
Section
Chapters