Estimation Of Solar Radiation By Artificial Networks: East Coast Malaysia

In this paper the solar radiation forecasting in Pekan located in Pahang is presented. The time series utilized are 10 minute solar radiation data obtained directly from the measurements realized in the sites during about one month. In order to do solar radiation forecasting, quick propagation alg...

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Main Authors:	K., Kadirgama, A. K., Amiruddin, R. A., Bakar
Format:	Article
Language:	English
Published:	Elsevier Ltd 2014
Subjects:	T Technology (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/8203/ http://umpir.ump.edu.my/id/eprint/8203/ http://umpir.ump.edu.my/id/eprint/8203/ http://umpir.ump.edu.my/id/eprint/8203/1/1-s2.0-S1876610214009527-main.pdf

id	ump-8203
recordtype	eprints
spelling	ump-82032018-11-29T08:23:38Z http://umpir.ump.edu.my/id/eprint/8203/ Estimation Of Solar Radiation By Artificial Networks: East Coast Malaysia K., Kadirgama A. K., Amiruddin R. A., Bakar T Technology (General) In this paper the solar radiation forecasting in Pekan located in Pahang is presented. The time series utilized are 10 minute solar radiation data obtained directly from the measurements realized in the sites during about one month. In order to do solar radiation forecasting, quick propagation algorithms Artificial Neural Network (ANN) models were developed. Around 1617 data’s are taken to train ANN. The effects of temperature, humidity, wind speed, wind chill, pressure and rain on solar radiation are discussed in this paper. The maximum mean absolute percentage error was found to be less than 7.74% and R-squared (R2) values were found to be about 98.9% for the testing stations. However, these values were found to be 5.398% and 97.9 % for the training stations. The trained and tested ANN models show greater accuracy for evaluating the solar radiation. The predicted solar potential values from the ANN are given in the form of table where included the other variables such as temperature, humidity, wind speed, wind chill, pressure and rain. This table is of prime importance for different working disciplines, like scientists, architects, meteorologists and solar engineers, in Malaysia. The predictions from the ANN models could enable scientists to locate and design solar energy systems in Malaysia and determine the best solar technology Elsevier Ltd 2014 Article PeerReviewed application/pdf en cc_by_nc_nd http://umpir.ump.edu.my/id/eprint/8203/1/1-s2.0-S1876610214009527-main.pdf K., Kadirgama and A. K., Amiruddin and R. A., Bakar (2014) Estimation Of Solar Radiation By Artificial Networks: East Coast Malaysia. Energy Procedia, 52. pp. 383-388. ISSN 1876-6102 http://dx.doi.org/10.1016/j.egypro.2014.07.090 DOI: 10.1016/j.egypro.2014.07.090
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	T Technology (General)
spellingShingle	T Technology (General) K., Kadirgama A. K., Amiruddin R. A., Bakar Estimation Of Solar Radiation By Artificial Networks: East Coast Malaysia
description	In this paper the solar radiation forecasting in Pekan located in Pahang is presented. The time series utilized are 10 minute solar radiation data obtained directly from the measurements realized in the sites during about one month. In order to do solar radiation forecasting, quick propagation algorithms Artificial Neural Network (ANN) models were developed. Around 1617 data’s are taken to train ANN. The effects of temperature, humidity, wind speed, wind chill, pressure and rain on solar radiation are discussed in this paper. The maximum mean absolute percentage error was found to be less than 7.74% and R-squared (R2) values were found to be about 98.9% for the testing stations. However, these values were found to be 5.398% and 97.9 % for the training stations. The trained and tested ANN models show greater accuracy for evaluating the solar radiation. The predicted solar potential values from the ANN are given in the form of table where included the other variables such as temperature, humidity, wind speed, wind chill, pressure and rain. This table is of prime importance for different working disciplines, like scientists, architects, meteorologists and solar engineers, in Malaysia. The predictions from the ANN models could enable scientists to locate and design solar energy systems in Malaysia and determine the best solar technology
format	Article
author	K., Kadirgama A. K., Amiruddin R. A., Bakar
author_facet	K., Kadirgama A. K., Amiruddin R. A., Bakar
author_sort	K., Kadirgama
title	Estimation Of Solar Radiation By Artificial Networks: East Coast Malaysia
title_short	Estimation Of Solar Radiation By Artificial Networks: East Coast Malaysia
title_full	Estimation Of Solar Radiation By Artificial Networks: East Coast Malaysia
title_fullStr	Estimation Of Solar Radiation By Artificial Networks: East Coast Malaysia
title_full_unstemmed	Estimation Of Solar Radiation By Artificial Networks: East Coast Malaysia
title_sort	estimation of solar radiation by artificial networks: east coast malaysia
publisher	Elsevier Ltd
publishDate	2014
url	http://umpir.ump.edu.my/id/eprint/8203/ http://umpir.ump.edu.my/id/eprint/8203/ http://umpir.ump.edu.my/id/eprint/8203/ http://umpir.ump.edu.my/id/eprint/8203/1/1-s2.0-S1876610214009527-main.pdf
first_indexed	2023-09-18T22:05:32Z
last_indexed	2023-09-18T22:05:32Z
_version_	1777414682699628544

Estimation Of Solar Radiation By Artificial Networks: East Coast Malaysia

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