Mohd Azri Abd Aziz
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Preferred name
Mohd Azri Abd Aziz
Official Name
Mohd Azri , Abd Aziz
Alternative Name
Aziz, Azri A.
Aziz, Azri Abdul
Aziz, Azri
Main Affiliation
Scopus Author ID
57192086971
Researcher ID
CCT-1519-2022

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  • Publication
    Fusion wind and solar generation forecasting via neural network
    ( 2021-08-27)
    Mahmoud Mustafa Yaseen Mohammed Al Asbahi
    ;
    Muhammad Naufal Mansor
    ;
    Mohd Rizal Manan
    ;
    Mohd Azri Abd Aziz
    ;
    Roejhan Md Kawi
    ;
    Farah Hanan Mohd Faudzi
    Wind and solar power are the most common renewable resources of energy and their usage for power generation is quickly growing all over the world. However, both wind and solar power are difficult to predict manually due to every time changes in weather condition; therefore. power output of wind and solar is associated with some uncertainty. A reliable wind-solar day ahead load prediction proposed in this paperwork to support a small microgrids system. The system is a combination of hardware of solar panel, wind turbine, hybrid charge controller, current sensor, voltage sensor circuit, battery, Arduino Mega and personal computer that is install with MATLAB along with artificial neural network model for load forecast. The prediction model is known as Feedforward back propagation (FFBP) artificial neural network (ANN), this method utilizes a learning relationship between wind-solar power output and predicted weather. The FFBP model trained ANN to recognize similar pattern and to predict the output power based on train and tested data and the results achieved 99.5 accuracy, 6.25% MAPE and 1.2 % MAD.
  • Publication
    Fusion wind and solar generation prototype design with Neural Network
    ( 2021-08-27)
    Mahmoud Mustafa Yaseen Mohammed Al Asbahi
    ;
    Muhammad Naufal Mansor
    ;
    Mohd Rizal Manan
    ;
    Mohd Azri Abd Aziz
    ;
    Roejhan Md Kawi
    ;
    Farah Hanan Mohd Faudzi
    Wind and solar power are the most common renewable resources of energy and their usage for power generation is quickly growing all over the world. However, both wind and solar power are difficult to predict manually due to every time changes in weather condition; therefore, power output of wind and solar is associated with some uncertainty. A reliable wind-solar day ahead load prediction with neural network was proposed to support a small microgrids system. All the system performance measurement such as sensitivity, specificity and accuracy give higher than 90%.