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Hourly irradiance forecasting for Peninsular Malaysia using dynamic neural network with preprocessed data

Accurate irradiance forecasting is one of the essential factor that helps facilitate the proliferation of grid-connected photovoltaic (GCPV) integration. In Malaysia, this topic has not been substantially explored. This paper attempts to investigate the use of neural network by using data obtained f...

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Main Authors: Baharin, K. A., Rahman, H. A., Hassan, M. Y., Kim, G. C.
Format: Conference or Workshop Item
Published: 2015
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/59302/
http://dx.doi.org/10.1109/SCOReD.2013.7002570
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spelling my.utm.593022021-12-13T02:40:59Z http://eprints.utm.my/id/eprint/59302/ Hourly irradiance forecasting for Peninsular Malaysia using dynamic neural network with preprocessed data Baharin, K. A. Rahman, H. A. Hassan, M. Y. Kim, G. C. TK Electrical engineering. Electronics Nuclear engineering Accurate irradiance forecasting is one of the essential factor that helps facilitate the proliferation of grid-connected photovoltaic (GCPV) integration. In Malaysia, this topic has not been substantially explored. This paper attempts to investigate the use of neural network by using data obtained from meteorological condition measurement in Sepang, Malaysia to forecast hourly values of solar radiation. The data is preprocessed to eliminate defective values and help achieve convergence in a faster and reliable manner. The methodology uses Nonlinear Autoregressive (NAR) network which utilises historical irradiance values of annual, quarterly, and monthly durations to predict future hourly irradiance. The result shows that the NAR network can predict hourly irradiance with satisfactory result and, in order to produce better forecasting, longer data timeframes is preferable. 2015 Conference or Workshop Item PeerReviewed Baharin, K. A. and Rahman, H. A. and Hassan, M. Y. and Kim, G. C. (2015) Hourly irradiance forecasting for Peninsular Malaysia using dynamic neural network with preprocessed data. In: 2013 11th IEEE Student Conference on Research and Development, SCOReD 2013, 16 - 17 December 2013, Putrajaya, Malaysia. http://dx.doi.org/10.1109/SCOReD.2013.7002570
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Baharin, K. A.
Rahman, H. A.
Hassan, M. Y.
Kim, G. C.
Hourly irradiance forecasting for Peninsular Malaysia using dynamic neural network with preprocessed data
description Accurate irradiance forecasting is one of the essential factor that helps facilitate the proliferation of grid-connected photovoltaic (GCPV) integration. In Malaysia, this topic has not been substantially explored. This paper attempts to investigate the use of neural network by using data obtained from meteorological condition measurement in Sepang, Malaysia to forecast hourly values of solar radiation. The data is preprocessed to eliminate defective values and help achieve convergence in a faster and reliable manner. The methodology uses Nonlinear Autoregressive (NAR) network which utilises historical irradiance values of annual, quarterly, and monthly durations to predict future hourly irradiance. The result shows that the NAR network can predict hourly irradiance with satisfactory result and, in order to produce better forecasting, longer data timeframes is preferable.
format Conference or Workshop Item
author Baharin, K. A.
Rahman, H. A.
Hassan, M. Y.
Kim, G. C.
author_facet Baharin, K. A.
Rahman, H. A.
Hassan, M. Y.
Kim, G. C.
author_sort Baharin, K. A.
title Hourly irradiance forecasting for Peninsular Malaysia using dynamic neural network with preprocessed data
title_short Hourly irradiance forecasting for Peninsular Malaysia using dynamic neural network with preprocessed data
title_full Hourly irradiance forecasting for Peninsular Malaysia using dynamic neural network with preprocessed data
title_fullStr Hourly irradiance forecasting for Peninsular Malaysia using dynamic neural network with preprocessed data
title_full_unstemmed Hourly irradiance forecasting for Peninsular Malaysia using dynamic neural network with preprocessed data
title_sort hourly irradiance forecasting for peninsular malaysia using dynamic neural network with preprocessed data
publishDate 2015
url http://eprints.utm.my/id/eprint/59302/
http://dx.doi.org/10.1109/SCOReD.2013.7002570
_version_ 1720436905612410880
score 13.211869

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