Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 4th International Conference on Materials Chemistry and Environmental Engineering

Series Vol. 63 , 09 May 2024


Open Access | Article

Wind speed prediction

Alvin Xianghan Li * 1
1 Nanjing Foreign Language School

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 63, 16-20
Published 09 May 2024. © 09 May 2024 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Alvin Xianghan Li. Wind speed prediction. ACE (2024) Vol. 63: 16-20. DOI: 10.54254/2755-2721/63/20240988.

Abstract

With the help of wind farms, wind energy is a vital renewable energy source that contributes significantly to the world’s energy balance. The lifespan and maintenance costs of wind turbines will be reduced with an accurate wind speed prediction. On the other hand, wind speed is highly volatile and unpredictable. Thus, it is essential to do research into creating complex models and algorithms for precise wind speed prediction. So far, some of the most promising models include Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Autoregressive Moving Average (ARMA). Python, as an advanced and versatile programming language, is exceptionally suited for scripting the algorithms of these sophisticated models. This paper will use the data from Austin Texas and apply a Support Vector Machine (SVM) for wind speed prediction involves several stages, including data collection, data preprocessing, model selection, model training, parameter optimization, model validation, and prediction. Wind energy resource optimisation, maintenance cost reduction, and total wind farm efficiency can all be significantly improved by incorporating these models into predictive analytics and continuously improving them against changing data.

Keywords

Wind energy, speed prediction, Support Vector Machine

References

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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 4th International Conference on Materials Chemistry and Environmental Engineering
ISBN (Print)
978-1-83558-417-0
ISBN (Online)
978-1-83558-418-7
Published Date
09 May 2024
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/63/20240988
Copyright
09 May 2024
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

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