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.

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