Artificial neural network technique to predict the power output of photovoltaic for monocrystalline and polycrystalline

Mohamed, Abdou Mani (2022) Artificial neural network technique to predict the power output of photovoltaic for monocrystalline and polycrystalline. Masters thesis, Universiti Tun Hussein Onn Malaysia.


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The demand for energy is predicted to rise rapidly in the near future as a result of population development and industrialization around the world. However, increased use of fossil fuels is responsible for the majority of environmental pollution and greenhouse gas emissions, which are widely believed to be the primary drivers of global warming and are contributing to it. This project represent the design of artificial neural network model (ANN) that predict the power output of the photovoltaic (PV) for monocrystalline and polycrystalline. The objectives of this project is to develop ANN model, to evaluate power and efficiency of two different photovoltaic panel. The data was collectedfrom 5 May 2018 to 6 May 2020. However, the input parameters are metreological data is used as input for ANN model. The voltage produced by polycrystalline is much more lager than monocrystalline voltage. In contrast, monocrystalline PV panel tend to have a higher current values compared to polycrystalline PV pnael. Mean square error (MSE) training of this model was equal to MSE testing and MSE validation. It means the data of model have been learning very well during training and zero means that it has an overestamte the prediction of the network. It is clear that the two models have a very good fit curve of the data as the correaltion coefficient, R value is equal to 1. However, the atual and predeictd values show a similarity in trends for both PV modules. The estimated voltage, current and power when compared to the actual value has no significant differences. Overall, polycrystalline panel has a better performance and the efficiency was 0.999% and 0.997% for moncrystalline.

Item Type: Thesis (Masters)
Subjects: T Technology > TJ Mechanical engineering and machinery > TJ212-225 Control engineering systems. Automatic machinery (General)
Divisions: Faculty of Electrical and Electronic Engineering > Department of Electrical Engineering
Depositing User: Mrs. Sabarina Che Mat
Date Deposited: 24 Apr 2022 00:26
Last Modified: 24 Apr 2022 00:26

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