Improved Artificial Neural Network for Grid-Connected Photovoltaic System Output Prediction

  • Authors

    • Shahril Irwan Sulaiman
    • Norfarizani Nordin
    • Ahmad Maliki Omar
  • photovoltaic, multi-layer feedforward neural network, solar irradiance, ambient temperature, module temperature.
  • This paper presents the output prediction of Grid-Connected Photovoltaic (GCPV) system using a multi-layer feedforward neural network. Conventional prediction requires mathematical expressions that need to be updated whenever new system is investigated. However, the introduction of Artificial Neural Network (ANN) in this study eliminated the need for using mathematical expressions. The MLFNN inputs were set to be Solar Irradiance (SI), Ambient Temperature (AT) and Module Temperature (MT) with the respective both current and previous five-minute values while the sole output was set to be the AC power from the GCPV system. The MLFNN was implemented in two stages, i.e. the training and testing. The results showed that the MLFNN model had outperformed the existing MLFNN model using SI and AT as inputs without previous five-minute values in producing the lowest Root Mean Square Error (RMSE) and highest correlation coefficient during both training and testing processes.



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  • How to Cite

    Irwan Sulaiman, S., Nordin, N., & Maliki Omar, A. (2019). Improved Artificial Neural Network for Grid-Connected Photovoltaic System Output Prediction. International Journal of Engineering & Technology, 8(1.7), 126-132.