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19500

COMPARISON BETWEEN TWO METHODS OF PREDICTION OF ELECTRIC POWER GENERATION FROM WIND POWER

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Last updated: 22 Jan 2023

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Abstract

More growth of wind power generation that will be established in Egypt in the coming years has highlighted the importance of wind power prediction. However, wind power is very difficult for modelling and forecasting. Despite the performed research works in the area, more efficient wind power forecast methods are still demanded. In this paper, two methods of prediction of electric power generation from wind power are presented. The first method is by using the artificial neural network for prediction of power generation in the next 10 minutes base on wind speed prediction input from weather authorities. The second method is by using poly fit function to perform regression on wind power by using MATLABĀ  program in Zafarana site. For optimum generation management strategy, the capacity credit will be used by the best selected method of prediction of the wind power for Gabal El-zeit site of future wind farm.

DOI

10.21608/auej.2016.19500

Keywords

Wind power forecast, Artificial Neural network (ANN), Poly fit function, Capacity Credit

Authors

First Name

Mohamed

Last Name

El-Mahlawy

MiddleName

Ahmed

Affiliation

Electrical Information Department, Ministry of Electricity and Renewable Energy, Cairo, Egypt

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First Name

Said

Last Name

Mekhamer

MiddleName

Fouad

Affiliation

Electrical Power and Machines Engineering Department, Ain Shams University, Cairo, Egypt

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Orcid

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First Name

Mohamed

Last Name

Badr

MiddleName

Abd-Elatif

Affiliation

Electrical Power and Machines Engineering Department, Ain Shams University, Cairo, Egypt2

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Volume

11

Article Issue

38

Related Issue

3968

Issue Date

2016-01-01

Receive Date

2018-11-18

Publish Date

2016-01-01

Page Start

159

Page End

163

Print ISSN

1687-8418

Link

https://jaes.journals.ekb.eg/article_19500.html

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https://jaes.journals.ekb.eg/service?article_code=19500

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25

Type

Original Article

Type Code

706

Publication Type

Journal

Publication Title

Journal of Al-Azhar University Engineering Sector

Publication Link

https://jaes.journals.ekb.eg/

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Article

Created At

22 Jan 2023