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

Mohd Fahmi, Natasha Munirah and Zambri, Nor Aira and Salim, Norhafiz and Sim, Sy Yi (2021) Power forecasting from solar panels using artificial neural network in UTHM Parit Raja. Journal of Advanced Industrial Technology and Application, 2 (1). pp. 18-27.

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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.

Item Type: Article
Uncontrolled Keywords: Photovoltaic energy; PV module; Simulink model; ANN
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1001-1841 Production of electric energy or power. Powerplants. Central stations
Depositing User: Mr. Abdul Rahim Mat Radzuan
Date Deposited: 22 Nov 2021 02:45
Last Modified: 22 Nov 2021 02:45
URI: http://eprints.uthm.edu.my/id/eprint/3767

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