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Estimator Model for Prediction of Power Output of Wave Farms Using Machine Learning Methods paper by Bhavana Burramukku
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Estimator Model for Prediction of Power Output of Wave Farms Using Machine Learning Methods

by Bhavana Burramukku ID: arxiv-paper--2011.13130

The amount of power generated by a wave farm depends on the Wave Energy Converter (WEC) arrangement along with the usual wave conditions. Therefore, forming the appropriate arrangement of WECs in an array is an important factor in maximizing power absorption. Data collected from the test sites is us...

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BibTeX 
@misc{arxiv_paper__2011.13130,
  author = {Bhavana Burramukku},
  title = {Estimator Model for Prediction of Power Output of Wave Farms Using Machine Learning Methods Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2011.13130v1}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
Bhavana Burramukku. (2026). Estimator Model for Prediction of Power Output of Wave Farms Using Machine Learning Methods [Paper]. Free2AITools. https://arxiv.org/abs/2011.13130v1

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πŸ“ Executive Summary

"The amount of power generated by a wave farm depends on the Wave Energy Converter (WEC) arrangement along with the usual wave conditions. Therefore, forming the appropriate arrangement of WECs in an array is an important factor in maximizing power absorption. Data collected from the test sites is used to design a neural model for predicting wave farm's power output generated. This paper focuses on developing a neural model for the prediction of wave energy based on the data set derived from t..."

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@article{Burramukku2020Estimator,
  title={Estimator Model for Prediction of Power Output of Wave Farms Using Machine Learning Methods},
  author={Bhavana Burramukku},
  journal={arXiv preprint arXiv:arxiv-paper--2011.13130},
  year={2020}
}

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Bhavana Burramukku

Abstract & Analysis

The amount of power generated by a wave farm depends on the Wave Energy Converter (WEC) arrangement along with the usual wave conditions. Therefore, forming the appropriate arrangement of WECs in an array is an important factor in maximizing power absorption. Data collected from the test sites is used to design a neural model for predicting wave farm's power output generated. This paper focuses on developing a neural model for the prediction of wave energy based on the data set derived from the four real wave scenarios from the southern coast of Australia. The applied converter model is a fully submerged three-tether converter called CETO. A precise analysis of the WEC placement is investigated to reveal the amount of power generated by the wave farms on the test site.

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arxiv-paper--2011.13130
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Bhavana Burramukku
tags
arxiv:eess.SParxiv:cs.NE

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