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Performance comparison of artificial intelligence techniques in short term current forecasting for photovoltaic system

International Journal of Power Electronics and Drive Systems · Vol. 10 No. 4 (2019) · hlm. 2148–2156

DOI
10.11591/ijpeds.v10.i4.pp2148-2156
ISSN
2722-256X, 2088-8694
Terbit
2019-12-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

This paper presents artificial intelligence approach of artificial neural network (ANN) and random forest (RF) that used to perform short-term photovoltaic (PV) output current forecasting (STPCF) for the next 24-hours. The input data for ANN and RF is consists of multiple time lags of hourly solar irradiance, temperature, hour, power and current to determine the movement pattern of data that have been denoised by using wavelet decomposition. The Levenberg-Marquardt optimization technique is used as a back-propagation algorithm for ANN and the bagging based bootstrapping technique is used in the RF to improve the results of forecasting. The information of PV output current is obtained from Green Energy Research (GERC) University Technology Mara Shah Alam, Malaysia and is used as the case study in estimation of PV output current for the next 24-hours. The results have shown that both proposed techniques are able to perform forecasting of future hourly PV output current with less error.

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  • Universiti Teknologi MARA
  • UCSI University
  • University Ferhat Abbas of Setif
  • National University of Malaysia
  • University of Brawijaya
  • HELP University
  • Universiti Tenaga Nasional
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Rekan penulisOrganisasiArtikel bersama
Zulkiffli Abdul HamidUniversiti Teknologi MARA5
Mohammad Lutfi OthmanUniversiti Putra Malaysia4
Siti Zaliha Mohammad NoorUniversiti Teknologi MARA3
Norazlan HashimUniversiti Teknologi MARA3
Ahmad Farid AbidinUniversiti Teknologi MARA3
Muhamad Faliq Mohamad NazerUCSI University2
Shahrizal JelaniUCSI University2
Mohd Helmi MansorUniversiti Tenaga Nasional2
Hadi SuyonoUniversity of Brawijaya2
Rini Nur HasanahUniversity of Brawijaya2
Muhammad Khairul Azman Mohd JamhariUniversiti Teknologi MARA2
Ammar Yasier AzmanUniversiti Teknologi MARA2
Muhammad Alif MansorUniversiti Teknologi MARA2
Nik Fasdi Nik IsmailUniversiti Teknologi MARA1
Dalina JohariUniversiti Teknologi MARA1
Azhan Ab RahmanUniversiti Teknikal Malaysia Melaka1
Ramzi KouadriUniversity Ferhat Abbas of Setif1
Linda SlimaniUniversity Ferhat Abbas of Setif1
Tarek BouktirUniversity Ferhat Abbas of Setif1
A.V.Senthil KumarAfiliasi tidak diketahui1
Muhammad Firdaus ShaariUniversiti Teknologi MARA1
Farah Adilah JamaludinUCSI University1
Nor Azura Md GhaniUniversiti Teknologi MARA1
Saadi Bin Ahmad KamaruddinHELP University1
Hazrita Ab RahimHELP University1
Nofri Yenita DahlanUniversiti Teknologi MARA1
Wan Nazirah Wan Md AdnanUniversity of Selangor1
Nor Azwan Mohamed KamariNational University of Malaysia1
Ahmad Asrul IbrahimNational University of Malaysia1
Syahirah Abd HalimNational University of Malaysia1
Fareed Danial Ahmad KaharUniversiti Teknologi MARA1
Saiful Izwan SulimanUniversiti Teknologi MARA1
Mohamed ZellaguiUniversity of Batna 11
Muhamad Hazim LokmanUniversiti Teknologi MARA1
Sharifah Azma Syed MustafaUniversiti Teknologi MARA1

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Sumber metadata: OAI-PMH (metadata jurnal) http://ijpeds.iaescore.com/index.php/IJPEDS/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Performance comparison of artificial intelligence techniques in short term current forecasting for photovoltaic system</dc:title>
	<dc:creator>Othman, Muhammad Murtadha</dc:creator>
	<dc:creator>Fazil, Mohammad Fazrul Ashraf Mohd</dc:creator>
	<dc:creator>Harun, Mohd Hafez Hilmi</dc:creator>
	<dc:creator>Musirin, Ismail</dc:creator>
	<dc:creator>Sulaiman, Shahril Irwan</dc:creator>
	<dc:description xml:lang="en-US">This paper presents artificial intelligence approach of artificial neural network (ANN) and random forest (RF) that used to perform short-term photovoltaic (PV) output current forecasting (STPCF) for the next 24-hours. The input data for ANN and RF is consists of multiple time lags of hourly solar irradiance, temperature, hour, power and current to determine the movement pattern of data that have been denoised by using wavelet decomposition. The Levenberg-Marquardt optimization technique is used as a back-propagation algorithm for ANN and the bagging based bootstrapping technique is used in the RF to improve the results of forecasting. The information of PV output current is obtained from Green Energy Research (GERC) University Technology Mara Shah Alam, Malaysia and is used as the case study in estimation of PV output current for the next 24-hours. The results have shown that both proposed techniques are able to perform forecasting of future hourly PV output current with less error.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2019-12-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijpeds.iaescore.com/index.php/IJPEDS/article/view/20085</dc:identifier>
	<dc:identifier>10.11591/ijpeds.v10.i4.pp2148-2156</dc:identifier>
	<dc:source xml:lang="en-US">International Journal of Power Electronics and Drive Systems (IJPEDS); Vol 10, No 4: December 2019; 2148-2156</dc:source>
	<dc:source>2722-256X</dc:source>
	<dc:source>2088-8694</dc:source>
	<dc:source>10.11591/ijpeds.v10.i4</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijpeds.iaescore.com/index.php/IJPEDS/article/view/20085/12948</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2019 Muhammad Murtadha Othman, Mohammad Fazrul Ashraf Mohd Fazil, Mohd Hafez Hilmi Harun, Ismail Musirin, Shahril Irwan Sulaiman</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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