Estimation of global solar radiation using artificial neural networks

M. Mohandes; S. Rehman; T. O. Halawani

doi:10.1016/S0960-1481(98)00065-2

Estimation of global solar radiation using artificial neural networks

M. Mohandes^*
, S. Rehman
, T. O. Halawani

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

331 Scopus citations

Abstract

This paper introduces a neural network technique for the estimation of global solar radiation. There are 41 radiation data collection stations spread all over the kingdom of Saudi Arabia where the radiation data and sunshine duration information are being collected since 1971. The available data from 31 locations is used for training the neural networks and the data from the other 10 locations is used for testing. The testing data was not used in the modeling to give an indication of the performance of the system in unknown locations. Results indicate the viability of this approach for spatial modeling of solar radiation.

Original language	English
Pages (from-to)	179-184
Number of pages	6
Journal	Renewable Energy
Volume	14
Issue number	1-4
DOIs	https://doi.org/10.1016/S0960-1481(98)00065-2
State	Published - 1998

Bibliographical note

Funding Information:
The authors wish to acknowledge the support of the Research Institute of King Fahd University of Petroleum & Minerals, Dhahran-31261, Saudi Arabia.

ASJC Scopus subject areas

Renewable Energy, Sustainability and the Environment
General Engineering

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10.1016/S0960-1481(98)00065-2

Highly Cited Paper Award 2022
Mohandes, M. (Recipient), Rehman, S. (Recipient) & Halawani, T. O. (Recipient), 2022
Prize

Cite this

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title = "Estimation of global solar radiation using artificial neural networks",

abstract = "This paper introduces a neural network technique for the estimation of global solar radiation. There are 41 radiation data collection stations spread all over the kingdom of Saudi Arabia where the radiation data and sunshine duration information are being collected since 1971. The available data from 31 locations is used for training the neural networks and the data from the other 10 locations is used for testing. The testing data was not used in the modeling to give an indication of the performance of the system in unknown locations. Results indicate the viability of this approach for spatial modeling of solar radiation.",

author = "M. Mohandes and S. Rehman and Halawani, \{T. O.\}",

note = "Funding Information: The authors wish to acknowledge the support of the Research Institute of King Fahd University of Petroleum \& Minerals, Dhahran-31261, Saudi Arabia.",

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language = "English",

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TY - JOUR

T1 - Estimation of global solar radiation using artificial neural networks

AU - Mohandes, M.

AU - Rehman, S.

AU - Halawani, T. O.

N1 - Funding Information: The authors wish to acknowledge the support of the Research Institute of King Fahd University of Petroleum & Minerals, Dhahran-31261, Saudi Arabia.

PY - 1998

Y1 - 1998

N2 - This paper introduces a neural network technique for the estimation of global solar radiation. There are 41 radiation data collection stations spread all over the kingdom of Saudi Arabia where the radiation data and sunshine duration information are being collected since 1971. The available data from 31 locations is used for training the neural networks and the data from the other 10 locations is used for testing. The testing data was not used in the modeling to give an indication of the performance of the system in unknown locations. Results indicate the viability of this approach for spatial modeling of solar radiation.

AB - This paper introduces a neural network technique for the estimation of global solar radiation. There are 41 radiation data collection stations spread all over the kingdom of Saudi Arabia where the radiation data and sunshine duration information are being collected since 1971. The available data from 31 locations is used for training the neural networks and the data from the other 10 locations is used for testing. The testing data was not used in the modeling to give an indication of the performance of the system in unknown locations. Results indicate the viability of this approach for spatial modeling of solar radiation.

UR - https://www.scopus.com/pages/publications/0032073494

U2 - 10.1016/S0960-1481(98)00065-2

DO - 10.1016/S0960-1481(98)00065-2

M3 - Conference article

AN - SCOPUS:0032073494

SN - 0960-1481

VL - 14

SP - 179

EP - 184

JO - Renewable Energy

JF - Renewable Energy

IS - 1-4

ER -

Estimation of global solar radiation using artificial neural networks

Abstract

Bibliographical note

ASJC Scopus subject areas

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Prizes

Highly Cited Paper Award 2022

Cite this