Vertical Wind Speed Estimation Using Generalized Additive Model (GAM) for Regression

Hilal H. Nuha; Rizka Reza Pahlevi; Mohamed Mohandes; S. Rehman; A. Al-Shaikhi; H. Tella

doi:10.1109/CICN56167.2022.10008372

Vertical Wind Speed Estimation Using Generalized Additive Model (GAM) for Regression

Hilal H. Nuha, Rizka Reza Pahlevi, Mohamed Mohandes, S. Rehman, A. Al-Shaikhi, H. Tella

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

The general plan for the provision of electricity of Indonesia Electricity Company for 2010-2019 states that the annual electricity demand is 55,000 MW. Wind speed (WS) assessment is required for wind farm site candidates. This paper uses the generalized additive model (GAM) for vertical WS estimation. The method is evaluated in terms of symmetric mean absolute percentage error (SMAPE), mean absolute error (MAE), and the adjusted coefficient of determination (R2adj). The highest values of R2adj between the measured and the estimated WS values achieved by GAM method at 60, 100, 140, and 180 m of heights are 96.34%, 81.66%, 64.68 %, and 62.90 % respectively.

Original language	English
Title of host publication	Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	691-695
Number of pages	5
ISBN (Electronic)	9781665487719
DOIs	https://doi.org/10.1109/CICN56167.2022.10008372
State	Published - 2022
Event	14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022 - Al-Khobar, Saudi Arabia Duration: 4 Dec 2022 → 6 Dec 2022

Publication series

Name	Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022

Conference

Conference	14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022
Country/Territory	Saudi Arabia
City	Al-Khobar
Period	4/12/22 → 6/12/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

Generalized Additive Model (GAM)
Regression
Vertical Wind Speed Estimation

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Computer Vision and Pattern Recognition

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/CICN56167.2022.10008372

Cite this

Nuha, H. H., Pahlevi, R. R., Mohandes, M., Rehman, S., Al-Shaikhi, A., & Tella, H. (2022). Vertical Wind Speed Estimation Using Generalized Additive Model (GAM) for Regression. In Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022 (pp. 691-695). (Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CICN56167.2022.10008372

Nuha, Hilal H. ; Pahlevi, Rizka Reza ; Mohandes, Mohamed et al. / Vertical Wind Speed Estimation Using Generalized Additive Model (GAM) for Regression. Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 691-695 (Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022).

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title = "Vertical Wind Speed Estimation Using Generalized Additive Model (GAM) for Regression",

abstract = "The general plan for the provision of electricity of Indonesia Electricity Company for 2010-2019 states that the annual electricity demand is 55,000 MW. Wind speed (WS) assessment is required for wind farm site candidates. This paper uses the generalized additive model (GAM) for vertical WS estimation. The method is evaluated in terms of symmetric mean absolute percentage error (SMAPE), mean absolute error (MAE), and the adjusted coefficient of determination (R2adj). The highest values of R2adj between the measured and the estimated WS values achieved by GAM method at 60, 100, 140, and 180 m of heights are 96.34%, 81.66%, 64.68 %, and 62.90 % respectively.",

keywords = "Generalized Additive Model (GAM), Regression, Vertical Wind Speed Estimation",

author = "Nuha, {Hilal H.} and Pahlevi, {Rizka Reza} and Mohamed Mohandes and S. Rehman and A. Al-Shaikhi and H. Tella",

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doi = "10.1109/CICN56167.2022.10008372",

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series = "Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022",

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Nuha, HH, Pahlevi, RR, Mohandes, M , Rehman, S , Al-Shaikhi, A & Tella, H 2022, Vertical Wind Speed Estimation Using Generalized Additive Model (GAM) for Regression. in Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022. Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022, Institute of Electrical and Electronics Engineers Inc., pp. 691-695, 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022, Al-Khobar, Saudi Arabia, 4/12/22. https://doi.org/10.1109/CICN56167.2022.10008372

Vertical Wind Speed Estimation Using Generalized Additive Model (GAM) for Regression. / Nuha, Hilal H.; Pahlevi, Rizka Reza; Mohandes, Mohamed et al.
Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 691-695 (Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Nuha, Hilal H.

AU - Pahlevi, Rizka Reza

AU - Mohandes, Mohamed

AU - Rehman, S.

AU - Al-Shaikhi, A.

AU - Tella, H.

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N2 - The general plan for the provision of electricity of Indonesia Electricity Company for 2010-2019 states that the annual electricity demand is 55,000 MW. Wind speed (WS) assessment is required for wind farm site candidates. This paper uses the generalized additive model (GAM) for vertical WS estimation. The method is evaluated in terms of symmetric mean absolute percentage error (SMAPE), mean absolute error (MAE), and the adjusted coefficient of determination (R2adj). The highest values of R2adj between the measured and the estimated WS values achieved by GAM method at 60, 100, 140, and 180 m of heights are 96.34%, 81.66%, 64.68 %, and 62.90 % respectively.

AB - The general plan for the provision of electricity of Indonesia Electricity Company for 2010-2019 states that the annual electricity demand is 55,000 MW. Wind speed (WS) assessment is required for wind farm site candidates. This paper uses the generalized additive model (GAM) for vertical WS estimation. The method is evaluated in terms of symmetric mean absolute percentage error (SMAPE), mean absolute error (MAE), and the adjusted coefficient of determination (R2adj). The highest values of R2adj between the measured and the estimated WS values achieved by GAM method at 60, 100, 140, and 180 m of heights are 96.34%, 81.66%, 64.68 %, and 62.90 % respectively.

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Nuha HH, Pahlevi RR, Mohandes M , Rehman S , Al-Shaikhi A, Tella H. Vertical Wind Speed Estimation Using Generalized Additive Model (GAM) for Regression. In Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 691-695. (Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022). doi: 10.1109/CICN56167.2022.10008372

Vertical Wind Speed Estimation Using Generalized Additive Model (GAM) for Regression

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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Cite this