Volume 9, Issue 2 (February 2022), Pages: 119-127

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 Original Research Paper

 Title: Turbine recommender: The selection of wind turbine type using one of a machine learning technique

 Author(s): Mayda Alrige *, Hind Bitar, Joud Aljaeed, Somaiah Alasmari

 Affiliation(s):

 Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-1315-7053

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2022.02.013

 Abstract:

This study aims to utilize the machine learning technique to build a model to recommend the suitable wind turbine type based on some variables, such as air speed and air density, as well as visualize the location of the recommended wind turbine selection on a 3D map. Particularly, we applied the K-nearest neighbor model (KNN) to determine the amount of energy produced by a single wind turbine. We applied it on 10 separate wind farms in Saudi Arabia. The results indicate that the model performs very well in predicting the best wind turbine type with the mean accuracy of 88%, where ten wind stations resulted from the optimized model with the suggested turbine type in each station. Adding more wind attributes and other factors may assist in increasing the model mean accuracy. The project’s findings will assist decision-makers in Saudi Arabia to make informed decisions as to what kind of wind turbine is suitable for a specific location. In the long run, this will help to make wind energy-a sustainable source of energy-one of the main goals of the 2030 vision, specifically under National Industrial Development and Logistics Program. 

 © 2022 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Turbine recommender, Machine learning, Modelling, Wind turbine energy, KNN

 Article History: Received 29 August 2021, Received in revised form 12 November 2021, Accepted 16 December 2021

 Acknowledgment 

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Alrige M, Bitar H, and Aljaeed J et al. (2022). Turbine recommender: The selection of wind turbine type using one of a machine learning technique. International Journal of Advanced and Applied Sciences, 9(2): 119-127

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 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 Fig. 9 Fig. 10 Fig. 11 Fig. 12 Fig. 13 

 Tables

 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7   

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