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Recurrent neural network-based electrical modeling and simulation of solar photovoltaic cell

International Journal of Applied Power Engineering (IJAPE) · Vol. 15 No. 3 (2026) · hlm. 1223–1232

DOI
10.11591/ijape.v15.i3.pp1223-1232
ISSN
2722-2624, 2252-8792
Terbit
2026-09-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Photovoltaics (PV) are an important renewable energy (RE) source and a key component of solar power plants. Accurate modeling of their electrical output characteristics is crucial for efficient system design, such as the current-voltage (I-V) and power-voltage (P-V) curves. These characteristic curves depend on the photovoltaic cell (PVC) parameters such as short circuit current (ISC), open circuit voltage (VOC), and maximum power (PMAX). Previous studies have explored the artificial neural network (ANN) in photovoltaics generation (PVG) systems to estimate the output power of the PVC. Nevertheless, most ANN-based approaches are limited to estimating output power only from the daily power generation data of the PVG system. In this paper, a PVC model was developed using a recurrent neural network (RNN) to increase the exactness estimation of electrical parameters. The proposed RNN-based PVC model employs solar irradiance (S), temperature (T), and series resistance (RS) as input variables, whereas the output variables are the power and current of PVC. The contributions of the PVC model is evaluated through simulations under changing physical and environmental conditions. The simulation yield of the RNN-based PVC model successfully reproduces I-V and P-V curves and also fundamental parameters consistent with the behavior of real PVC.

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  • Universitas Bhayangkara Surabaya
  • Universitas Bhayangkara Jakarta Raya
  • Institut Agama Islam Negeri Parepare
  • Institut Sains dan Teknologi Terpadu Surabaya
  • California Polytechnic State University
  • Universitas Airlangga
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Rekan penulisOrganisasiArtikel bersama
Amirullah AmirullahUniversitas Bhayangkara Surabaya3
Kuspijani KuspijaniUniversitas Bhayangkara Jakarta Raya2
Ahmadi AhmadiUniversitas Bhayangkara Surabaya1
Adiananda AdianandaUniversitas Bhayangkara Surabaya1
Saidah SaidahInstitut Agama Islam Negeri Parepare1
Taufik TaufikCalifornia Polytechnic State University1
Hari SutiksnoInstitut Sains dan Teknologi Terpadu Surabaya1
Saidah BaisaUniversitas Bhayangkara Jakarta Raya1
Soebagio SoebagioUniversitas Airlangga1
M. AshariAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) http://ijape.iaescore.com/index.php/IJAPE/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">Recurrent neural network-based electrical modeling and simulation of solar photovoltaic cell</dc:title>
	<dc:creator>Purwahyudi, Bambang</dc:creator>
	<dc:creator>Kiswantono, Agus</dc:creator>
	<dc:creator>Afianti, Hasti</dc:creator>
	<dc:creator>Saidah, Saidah</dc:creator>
	<dc:subject xml:lang="en-US">I-V characteristics; photovoltaic cell; P-V characteristic; recurrent neural network; renewable energy</dc:subject>
	<dc:description xml:lang="en-US">Photovoltaics (PV) are an important renewable energy (RE) source and a key component of solar power plants. Accurate modeling of their electrical output characteristics is crucial for efficient system design, such as the current-voltage (I-V) and power-voltage (P-V) curves. These characteristic curves depend on the photovoltaic cell (PVC) parameters such as short circuit current (ISC), open circuit voltage (VOC), and maximum power (PMAX). Previous studies have explored the artificial neural network (ANN) in photovoltaics generation (PVG) systems to estimate the output power of the PVC. Nevertheless, most ANN-based approaches are limited to estimating output power only from the daily power generation data of the PVG system. In this paper, a PVC model was developed using a recurrent neural network (RNN) to increase the exactness estimation of electrical parameters. The proposed RNN-based PVC model employs solar irradiance (S), temperature (T), and series resistance (RS) as input variables, whereas the output variables are the power and current of PVC. The contributions of the PVC model is evaluated through simulations under changing physical and environmental conditions. The simulation yield of the RNN-based PVC model successfully reproduces I-V and P-V curves and also fundamental parameters consistent with the behavior of real PVC.</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>2026-09-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijape.iaescore.com/index.php/IJAPE/article/view/22132</dc:identifier>
	<dc:identifier>10.11591/ijape.v15.i3.pp1223-1232</dc:identifier>
	<dc:source xml:lang="en-US">International Journal of Applied Power Engineering (IJAPE); Vol 15, No 3: September 2026; 1223-1232</dc:source>
	<dc:source>2722-2624</dc:source>
	<dc:source>2252-8792</dc:source>
	<dc:source>10.11591/ijape.v15.i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijape.iaescore.com/index.php/IJAPE/article/view/22132/13498</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Bambang Purwahyudi, Agus Kiswantono, Hasti Afianti, Saidah</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>