Prediction of photovoltaic (PV) output via artificial neural network (ANN) based on real climate condition
Photovoltaic (PV) system most popular as harvesting energy and has major challenged due to the difficult to control the output. The performance of PV panel output is incompatible due to changing climate condition. It difficult to knowing the power output of PV panel system for multiplicity amount of...
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| Format: | Thesis |
| Published: |
2017
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/9203/ http://eprints.uthm.edu.my/9203/1/Flora_Anak_Crocker.pdf |
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| Summary: | Photovoltaic (PV) system most popular as harvesting energy and has major
challenged due to the difficult to control the output. The performance of PV panel
output is incompatible due to changing climate condition. It difficult to knowing the
power output of PV panel system for multiplicity amount of solar radiation and the
ambient environmental factors. Due to solar radiation, data is not always available in
remote areas; it would be assisted if the solar radiation from the system could be
predicted. This study will explore the potential of artificial neural network (ANN) to
be applying in the system of prediction of power output from photovoltaic (PV)
panel system. In order to test the efficiency and reliability of a proposed ANN model
experimental output compare with proposed mathematical equation. The objectives
of this project are to develop the ANN model that capable of predicting power
output, obtain power forecast model using ambient factor and identify the influence
of climate changing for electrical production. The proposal of this study to predict
the solar radiation, voltage, current, and power based on the real climate condition
along with PV panel system. The activation functions using for the hidden layer is
hyperbolic tangent. The training algorithm is used Levenberg-Marquardt
backpropagation. The meteorology data as input data was obtained from RET screen
database in the period from 1st January 2015 until 31st August 2016. There was five
selected location in Malaysia to be the subject test. From the result, average power
output was high level in January to March for all selected location except for
Kuching. While in low average solar radiation, the power output was at a low level.
This shows that the performance of power output is depending on the level of solar
radiation. |
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