Power forecasting from solar panels using artificial neural network in UTHM Parit Raja

• Main Authors: Mohd Fahmi, Natasha Munirah, Zambri, Nor Aira, Salim, Norhafiz, Sim, Sy Yi
• Format: Article
• Language: English
• Published: Penerbit UTHM 2021
• Subjects: TK1001-1841 Production of electric energy or power. Powerplants. Central stations
• Online Access: http://eprints.uthm.edu.my/3767/1/J12597_61fed87ff2381d431f0f6d79715fe91f.pdf; http://eprints.uthm.edu.my/3767/; https://doi.org/10.30880/jaita.2021.02.01.003

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.