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Machine Learning Method for Solar PV Output Power Prediction

Article

Last updated: 04 Jan 2025

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Subjects

-

Tags

Electrical Engineering : Electric power generation, transmission, dist…d generation and micro grid, communication, control engineering, etc.

Abstract

To deal with the challenges of the solar photovoltaic (PV) energy source due to the continuous variations of the climatic conditions such as temperature and solar radiation, output power prediction is one of the most important research trends nowadays. In this paper, a multilayer feedforward neural network (MLFFNN) is executed to foresee the power for a solar PV power station. The MLFFNN employs the temperature and radiation as the inputs and the power as the output. For training and testing the MLFFNN, data of 6 days are acquired from a real PV power station in Egypt. The first five days are employed to train the MLFFNN using Levenberg-Marquardt (LM) algorithm. While the data of the sixth day, are used to check the effectiveness and the generalization ability of the trained MLFFNN. The results prove that the trained MLFFNN is working very well and efficient to predict the PV output power correctly.

DOI

10.21608/svusrc.2022.157039.1066

Keywords

Power Prediction, Multilayer Feedforward NN, Solar PV, Levenberg-Marquardt Algorithm, MLFFNN Effectiveness

Authors

First Name

Abdel-Nasser

Last Name

Sharkawy

MiddleName

-

Affiliation

Mechatronics Engineering, Mechanical Engineering Department, Faculty of Engineering, South Valley University, Qena 83523, Egypt

Email

eng.abdelnassersharkawy@gmail.com

City

Qena

Orcid

0000-0001-9733-221X

First Name

Mustafa

Last Name

Ali

MiddleName

M.

Affiliation

Mechatronics Engineering, Department of Mechanical Engineering, South Valley University, Qena 83523, Egypt

Email

m.ali@eng.svu.edu.eg

City

Qena

Orcid

-

First Name

Hossam

Last Name

Mousa

MiddleName

H. H.

Affiliation

Department of Electrical Engineering, South Valley University, Qena 83523, Egypt

Email

hossam.herzallah7@gmail.com

City

Qena

Orcid

0000-0003-4753-2998

First Name

Ahmed

Last Name

Ali

MiddleName

S.

Affiliation

Mechatronics Engineering, Department of Mechanical Engineering, Assiut University, Assiut, Egypt

Email

ahmadsaad01@yahoo.com

City

Assiut

Orcid

-

First Name

G.

Last Name

Abdel-Jaber

MiddleName

T.

Affiliation

Department of Electrical Engineering, South Valley University, Qena 83523, Egypt

Email

gtag2000@yahoo.com

City

Qena

Orcid

-

Volume

3

Article Issue

2

Related Issue

34856

Issue Date

2022-12-01

Receive Date

2022-08-18

Publish Date

2022-12-01

Page Start

123

Page End

130

Print ISSN

2785-9967

Online ISSN

2735-4571

Article File

SVUSRC_Volume 3_Issue 2_Pages 123-130.pdf

PDF . 1.3MB

Link

https://svusrc.journals.ekb.eg/article_256624.html

Detail API

https://svusrc.journals.ekb.eg/service?article_code=256624

Order

12

Type

Original research articles

Type Code

1,585

Publication Type

Journal

Publication Title

SVU-International Journal of Engineering Sciences and Applications

Publication Link

https://svusrc.journals.ekb.eg/

MainTitle

Machine Learning Method for Solar PV Output Power Prediction

Details

Type

Article

Created At

23 Jan 2023