Authors :
Newsha Valadbeygi
Volume/Issue :
Volume 8 - 2023, Issue 9 - September
Google Scholar :
https://tinyurl.com/yc638d9a
Scribd :
https://tinyurl.com/366v2xyz
DOI :
https://doi.org/10.5281/zenodo.8420692
Abstract :
The assessment of wind energy potential is a
topic that has not yet been thoroughly explored. The
complex nature of wind potential makes it challenging to
predict and evaluate. To address this issue, researchers
have proposed various methods, including the use of
artificial intelligence and algorithms. In this study,
experimental data on wind energy conversion system
performance was utilized to develop a new model based
on neural networks. Specifically, a CNN-type neural
network was employed due to its high capacity. Three
different artificial neural network architectures were
designed, trained, and evaluated for wind speed
prediction. The proposed neural network model was
validated by comparing it with experimental data,
allowing for the prediction and analysis of wind energy
conversion system performance and trends. The results
indicate that the proposed model demonstrates a low
error percentage (ranging from 1 to 0.97) in predicting
the increasing efficiency of the wind energy conversion
system, suggesting its strong alignment with the real
model and network efficiency.
Keywords :
Maximum Power, Wind Energy Conversion, CNN Neural Network.
The assessment of wind energy potential is a
topic that has not yet been thoroughly explored. The
complex nature of wind potential makes it challenging to
predict and evaluate. To address this issue, researchers
have proposed various methods, including the use of
artificial intelligence and algorithms. In this study,
experimental data on wind energy conversion system
performance was utilized to develop a new model based
on neural networks. Specifically, a CNN-type neural
network was employed due to its high capacity. Three
different artificial neural network architectures were
designed, trained, and evaluated for wind speed
prediction. The proposed neural network model was
validated by comparing it with experimental data,
allowing for the prediction and analysis of wind energy
conversion system performance and trends. The results
indicate that the proposed model demonstrates a low
error percentage (ranging from 1 to 0.97) in predicting
the increasing efficiency of the wind energy conversion
system, suggesting its strong alignment with the real
model and network efficiency.
Keywords :
Maximum Power, Wind Energy Conversion, CNN Neural Network.
