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Modelling of the Output Power from a Grid-Connected Photovoltaic System Using the Artificial Neural Network

  • Shahril Irwan Sulaiman
  • Sulaiman Shaari
  • Ahmad Maliki Omar
Conference paper

Abstract

This paper presents the modelling of the output power of grid-connected photovoltaic (GCPV) system using the multilayer feedforward neural network (MLFNN). Different combinations of solar irradiance, ambient temperature and module temperature were derived for determining the best type of parameters which could be used to model the output power using the MLFNN. The MLFNN was implemented in two stages, i.e. training and testing, and all data were obtained from a retrofitted-based GCPV system. The results show that the MLFNN model with solar irradiance and ambient temperature had outperformed the model with solar irradiance and module temperature as well as the model with solar irradiance, ambient temperature and module temperature in producing higher modelling accuracy.

Keywords

Root Mean Square Error Artificial Neural Network Hide Layer Output Layer Solar Irradiance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Japan 2015

Authors and Affiliations

  • Shahril Irwan Sulaiman
    • 1
  • Sulaiman Shaari
    • 2
  • Ahmad Maliki Omar
    • 1
  1. 1.Faculty of Electrical EngineeringUniversiti Teknologi MARAShah AlamMalaysia
  2. 2.Faculty of Applied SciencesUniversiti Teknologi MARAShah AlamMalaysia

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