Implementation of a topographic artificial neural network wind speed prediction model for assessing onshore wind power potential in Sibu, Sarawak
This study presents a topographic machine learning based wind speed prediction model. Predicted and ground station data were used to examine the wind energy potential in Sibu. A terrain-based artificial neural network was developed using MATLAB/Simulink (2016). It was found that the developed model...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier Ltd.
2019
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/28686/1/Lawan.pdf http://ir.unimas.my/id/eprint/28686/ https://www.sciencedirect.com/science/article/pii/S1110982317304015 |
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| Institution: | Universiti Malaysia Sarawak |
| Language: | English |
| Summary: | This study presents a topographic machine learning based wind speed prediction model. Predicted and ground station data were used to examine the wind energy potential in Sibu. A terrain-based artificial neural network was developed using MATLAB/Simulink (2016). It was found that the developed model can predict wind speed values in areas where the model was implemented. The detailed wind resource assessment shows that the power and energy densities fall within Class 1, which is suitable for smallscale applications. The annual energy output of the selected wind turbines was found to be 2343.12–
12036.85 kWh/year with an annual capacity factor in the range of 2.16%–7.77%. |
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