Solar energy prediction for Malaysia using artificial neural networks

Tamer Khatib, Azah Mohamed, Kamaruzzaman Sopian, M. Mahmoud

Research output: Contribution to journalArticle

40 Citations (Scopus)

Abstract

This paper presents a solar energy prediction method using artificial neural networks (ANNs). An ANN predicts a clearness index that is used to calculate global and diffuse solar irradiations. The ANN model is based on the feed forward multilayer perception model with four inputs and one output. The inputs are latitude, longitude, day number, and sunshine ratio; the output is the clearness index. Data from 28 weather stations were used in this research, and 23 stations were used to train the network, while 5 stations were used to test the network. In addition, the measured solar irradiations from the sites were used to derive an equation to calculate the diffused solar irradiation, a function of the global solar irradiation and the clearness index. The proposed equation has reduced the mean absolute percentage error (MAPE) in estimating the diffused solar irradiation compared with the conventional equation. Based on the results, the average MAPE, mean bias error and root mean square error for the predicted global solar irradiation are 5.92%, 1.46%, and 7.96%. The MAPE in estimating the diffused solar irradiation is 9.8%. A comparison with previous work was done, and the proposed approach was found to be more efficient and accurate than previous methods.

Original languageEnglish
Article number419504
JournalInternational Journal of Photoenergy
Volume2012
DOIs
Publication statusPublished - 2012

Fingerprint

Malaysia
solar energy
Solar energy
Irradiation
Neural networks
irradiation
predictions
estimating
stations
weather stations
root-mean-square errors
output
longitude
Mean square error
Multilayers

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Chemistry(all)
  • Atomic and Molecular Physics, and Optics
  • Materials Science(all)

Cite this

Solar energy prediction for Malaysia using artificial neural networks. / Khatib, Tamer; Mohamed, Azah; Sopian, Kamaruzzaman; Mahmoud, M.

In: International Journal of Photoenergy, Vol. 2012, 419504, 2012.

Research output: Contribution to journalArticle

@article{d38ce043640a4a32bc5dfa7e5fc10e07,
title = "Solar energy prediction for Malaysia using artificial neural networks",
abstract = "This paper presents a solar energy prediction method using artificial neural networks (ANNs). An ANN predicts a clearness index that is used to calculate global and diffuse solar irradiations. The ANN model is based on the feed forward multilayer perception model with four inputs and one output. The inputs are latitude, longitude, day number, and sunshine ratio; the output is the clearness index. Data from 28 weather stations were used in this research, and 23 stations were used to train the network, while 5 stations were used to test the network. In addition, the measured solar irradiations from the sites were used to derive an equation to calculate the diffused solar irradiation, a function of the global solar irradiation and the clearness index. The proposed equation has reduced the mean absolute percentage error (MAPE) in estimating the diffused solar irradiation compared with the conventional equation. Based on the results, the average MAPE, mean bias error and root mean square error for the predicted global solar irradiation are 5.92{\%}, 1.46{\%}, and 7.96{\%}. The MAPE in estimating the diffused solar irradiation is 9.8{\%}. A comparison with previous work was done, and the proposed approach was found to be more efficient and accurate than previous methods.",
author = "Tamer Khatib and Azah Mohamed and Kamaruzzaman Sopian and M. Mahmoud",
year = "2012",
doi = "10.1155/2012/419504",
language = "English",
volume = "2012",
journal = "International Journal of Photoenergy",
issn = "1110-662X",
publisher = "Hindawi Publishing Corporation",

}

TY - JOUR

T1 - Solar energy prediction for Malaysia using artificial neural networks

AU - Khatib, Tamer

AU - Mohamed, Azah

AU - Sopian, Kamaruzzaman

AU - Mahmoud, M.

PY - 2012

Y1 - 2012

N2 - This paper presents a solar energy prediction method using artificial neural networks (ANNs). An ANN predicts a clearness index that is used to calculate global and diffuse solar irradiations. The ANN model is based on the feed forward multilayer perception model with four inputs and one output. The inputs are latitude, longitude, day number, and sunshine ratio; the output is the clearness index. Data from 28 weather stations were used in this research, and 23 stations were used to train the network, while 5 stations were used to test the network. In addition, the measured solar irradiations from the sites were used to derive an equation to calculate the diffused solar irradiation, a function of the global solar irradiation and the clearness index. The proposed equation has reduced the mean absolute percentage error (MAPE) in estimating the diffused solar irradiation compared with the conventional equation. Based on the results, the average MAPE, mean bias error and root mean square error for the predicted global solar irradiation are 5.92%, 1.46%, and 7.96%. The MAPE in estimating the diffused solar irradiation is 9.8%. A comparison with previous work was done, and the proposed approach was found to be more efficient and accurate than previous methods.

AB - This paper presents a solar energy prediction method using artificial neural networks (ANNs). An ANN predicts a clearness index that is used to calculate global and diffuse solar irradiations. The ANN model is based on the feed forward multilayer perception model with four inputs and one output. The inputs are latitude, longitude, day number, and sunshine ratio; the output is the clearness index. Data from 28 weather stations were used in this research, and 23 stations were used to train the network, while 5 stations were used to test the network. In addition, the measured solar irradiations from the sites were used to derive an equation to calculate the diffused solar irradiation, a function of the global solar irradiation and the clearness index. The proposed equation has reduced the mean absolute percentage error (MAPE) in estimating the diffused solar irradiation compared with the conventional equation. Based on the results, the average MAPE, mean bias error and root mean square error for the predicted global solar irradiation are 5.92%, 1.46%, and 7.96%. The MAPE in estimating the diffused solar irradiation is 9.8%. A comparison with previous work was done, and the proposed approach was found to be more efficient and accurate than previous methods.

UR - http://www.scopus.com/inward/record.url?scp=84863671986&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84863671986&partnerID=8YFLogxK

U2 - 10.1155/2012/419504

DO - 10.1155/2012/419504

M3 - Article

AN - SCOPUS:84863671986

VL - 2012

JO - International Journal of Photoenergy

JF - International Journal of Photoenergy

SN - 1110-662X

M1 - 419504

ER -