Power Forecasting from Solar Panels Using Artificial Neural Network in UTHM Parit Raja


  • Natasha Munirah Mohd Fahmi universiti tun hussein onn malaysia
  • Nor Aira Zambri Universiti Tun Hussein Onn Malaysia
  • Norhafiz Salim Universiti Teknikal Malaysia Melaka (UTeM), 76100 Durian Tunggal, Melaka
  • Sim Sy Yi Universiti Tun Hussein Onn Malaysia


photovoltaic energy, PV Module, Simulink model, ANN


This paper presents a step-by-step procedure for the simulation of photovoltaic modules with numerical values, using MALTAB/Simulink software. The proposed model is developed based on the mathematical model of PV module, which based on PV solar cell employing one-diode equivalent circuit. The output current and power characteristics curves highly depend on some climatic factors such as radiation and temperature, are obtained by simulation of the selected module. The collected data are used in developing Artificial Neural Network (ANN) model. Multilayer Perceptron (MLP) and Radial Basis Function (RBF) are the techniques used to forecast the outputs of the PV. Various types of activation function will be applied such as Linear, Logistic Sigmoid, Hyperbolic Tangent Sigmoid and Gaussian. The simulation results show that the Logistic Sigmoid is the best technique which produce minimal root mean square error for the system.




How to Cite

Mohd Fahmi, N. M., Zambri, N. A. ., Salim, N. ., & Yi, S. S. . (2021). Power Forecasting from Solar Panels Using Artificial Neural Network in UTHM Parit Raja. Journal of Advanced Industrial Technology and Application, 2(1), 18–27. Retrieved from https://publisher.uthm.edu.my/ojs/index.php/jaita/article/view/8828