A new approach for meteorological variables prediction at Kuala Lumpur, Malaysia, using artificial neural networks: Application for sizing and maintaining photovoltaic systems

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

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

This research presents a new meteorological variables prediction approach for Malaysia using artificial neural networks. The developed model predicts four meteorological variables using sun shine ratio, day number, and location coordinates. These meteorological variables are solar energy, ambient temperature, wind speed, and relative humidity. However, three statistical values are used to evaluate the proposed model. These statistical values are mean absolute percentage error (MAPE), mean bias error (MBE), and root mean square error (RMSE). Based on results, the developed model predicts accurately the four meteorological variables. The MAPE, RMSE, and MBE in predicting solar radiation are 1.3%, 5.8 (1.8%), and 0.9 (0.3%), respectively, while the MAPE, RMSE, and MBE values for ambient temperature prediction are 1.3%, 0.4 (1.7%), and 0.1 (0.4%), respectively. In addition, the MAPE, RMSE, and MBE values in relative humidity prediction are 3.2%, 3.2, and 0.2. As for wind speed prediction, it is the worst in accuracy among the predicted variables because the MAPE, RMSE, and MBE values are 28.9%, 0.5 (31.3%), and 0.02 (1.25%). Such a developed model helps in sizing photovoltaic (PV) systems using solar energy and ambient temperature records. Moreover, wind speed and relative humidity records could be used in estimating dust concentration group which leads to dust deposition on a PV system.

Original languageEnglish
Article number021005
JournalJournal of Solar Energy Engineering, Transactions of the ASME
Volume134
Issue number2
DOIs
Publication statusPublished - 2012

Fingerprint

Neural networks
Mean square error
Atmospheric humidity
Solar energy
Dust
Solar radiation
Sun
Temperature

Keywords

  • ambient temperature
  • meteorological variables prediction
  • PV systems
  • relative humidity
  • solar energy
  • wind speed

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment

Cite this

@article{47d87783b6ea4b4ead679d1287ee2cfd,
title = "A new approach for meteorological variables prediction at Kuala Lumpur, Malaysia, using artificial neural networks: Application for sizing and maintaining photovoltaic systems",
abstract = "This research presents a new meteorological variables prediction approach for Malaysia using artificial neural networks. The developed model predicts four meteorological variables using sun shine ratio, day number, and location coordinates. These meteorological variables are solar energy, ambient temperature, wind speed, and relative humidity. However, three statistical values are used to evaluate the proposed model. These statistical values are mean absolute percentage error (MAPE), mean bias error (MBE), and root mean square error (RMSE). Based on results, the developed model predicts accurately the four meteorological variables. The MAPE, RMSE, and MBE in predicting solar radiation are 1.3{\%}, 5.8 (1.8{\%}), and 0.9 (0.3{\%}), respectively, while the MAPE, RMSE, and MBE values for ambient temperature prediction are 1.3{\%}, 0.4 (1.7{\%}), and 0.1 (0.4{\%}), respectively. In addition, the MAPE, RMSE, and MBE values in relative humidity prediction are 3.2{\%}, 3.2, and 0.2. As for wind speed prediction, it is the worst in accuracy among the predicted variables because the MAPE, RMSE, and MBE values are 28.9{\%}, 0.5 (31.3{\%}), and 0.02 (1.25{\%}). Such a developed model helps in sizing photovoltaic (PV) systems using solar energy and ambient temperature records. Moreover, wind speed and relative humidity records could be used in estimating dust concentration group which leads to dust deposition on a PV system.",
keywords = "ambient temperature, meteorological variables prediction, PV systems, relative humidity, solar energy, wind speed",
author = "Tamer Khatib and Azah Mohamed and M. Mahmoud and Kamaruzzaman Sopian",
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journal = "Journal of Solar Energy Engineering, Transactions of the ASME",
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TY - JOUR

T1 - A new approach for meteorological variables prediction at Kuala Lumpur, Malaysia, using artificial neural networks

T2 - Application for sizing and maintaining photovoltaic systems

AU - Khatib, Tamer

AU - Mohamed, Azah

AU - Mahmoud, M.

AU - Sopian, Kamaruzzaman

PY - 2012

Y1 - 2012

N2 - This research presents a new meteorological variables prediction approach for Malaysia using artificial neural networks. The developed model predicts four meteorological variables using sun shine ratio, day number, and location coordinates. These meteorological variables are solar energy, ambient temperature, wind speed, and relative humidity. However, three statistical values are used to evaluate the proposed model. These statistical values are mean absolute percentage error (MAPE), mean bias error (MBE), and root mean square error (RMSE). Based on results, the developed model predicts accurately the four meteorological variables. The MAPE, RMSE, and MBE in predicting solar radiation are 1.3%, 5.8 (1.8%), and 0.9 (0.3%), respectively, while the MAPE, RMSE, and MBE values for ambient temperature prediction are 1.3%, 0.4 (1.7%), and 0.1 (0.4%), respectively. In addition, the MAPE, RMSE, and MBE values in relative humidity prediction are 3.2%, 3.2, and 0.2. As for wind speed prediction, it is the worst in accuracy among the predicted variables because the MAPE, RMSE, and MBE values are 28.9%, 0.5 (31.3%), and 0.02 (1.25%). Such a developed model helps in sizing photovoltaic (PV) systems using solar energy and ambient temperature records. Moreover, wind speed and relative humidity records could be used in estimating dust concentration group which leads to dust deposition on a PV system.

AB - This research presents a new meteorological variables prediction approach for Malaysia using artificial neural networks. The developed model predicts four meteorological variables using sun shine ratio, day number, and location coordinates. These meteorological variables are solar energy, ambient temperature, wind speed, and relative humidity. However, three statistical values are used to evaluate the proposed model. These statistical values are mean absolute percentage error (MAPE), mean bias error (MBE), and root mean square error (RMSE). Based on results, the developed model predicts accurately the four meteorological variables. The MAPE, RMSE, and MBE in predicting solar radiation are 1.3%, 5.8 (1.8%), and 0.9 (0.3%), respectively, while the MAPE, RMSE, and MBE values for ambient temperature prediction are 1.3%, 0.4 (1.7%), and 0.1 (0.4%), respectively. In addition, the MAPE, RMSE, and MBE values in relative humidity prediction are 3.2%, 3.2, and 0.2. As for wind speed prediction, it is the worst in accuracy among the predicted variables because the MAPE, RMSE, and MBE values are 28.9%, 0.5 (31.3%), and 0.02 (1.25%). Such a developed model helps in sizing photovoltaic (PV) systems using solar energy and ambient temperature records. Moreover, wind speed and relative humidity records could be used in estimating dust concentration group which leads to dust deposition on a PV system.

KW - ambient temperature

KW - meteorological variables prediction

KW - PV systems

KW - relative humidity

KW - solar energy

KW - wind speed

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