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Deep neural network for maximizing output power estimation of dual-axis solar tracker

IAES International Journal of Artificial Intelligence · Vol. 14 No. 3 (2025) · hlm. 2229–2235

DOI
10.11591/ijai.v14.i3.pp2229-2235
ISSN
2252-8938, 2089-4872
Terbit
2025-06-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

The abundance of solar energy sources has encouraged many researchers to maximize solar photovoltaic (PV) output power using dual-axis solar tracking. However, environmental conditions, time of day, and the angle of movement of the solar tracker can affect the resulting power output. This study aims to predict the power output of dual-axis solar tracking in order to maintain the power’s stability and quality. Deep neural networks (DNN) with variations of 5 and 6 hidden layers have been proposed. The dataset used in this study was obtained from observation results and then divided into 80% training data and 20% testing data. A series of algorithms are used to recognize relationship patterns between input and hidden layers, between hidden layers, as well as hidden layers and output. Statistical results show that DNN with a variation of 6 hidden layers is better at estimating solar tracking power output with a mean absolute percentage error (MAPE) value of 12.328%, mean square error (MSE) of 0.332, and mean absolute error (MAE) of 0.425. This study can be used as a reference in utilizing artificial intelligence to predict the output power of solar panels as a renewable energy source.

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  • Lampung University
  • Diponegoro University
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Rekan penulisOrganisasiArtikel bersama
Junaidi JunaidiLampung University2
Amir SupriyantoLampung University2
Suryono SuryonoDiponegoro University1
Jatmiko Endro SusenoDiponegoro University1

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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Deep neural network for maximizing output power estimation of dual-axis solar tracker</dc:title>
	<dc:creator>Ratu Ayu, Humairoh</dc:creator>
	<dc:creator>Mohamad Kurniawansyah, Rifki</dc:creator>
	<dc:creator>Risma Diansari, Aqua</dc:creator>
	<dc:subject xml:lang="en-US">Neural network; deep learning</dc:subject>
	<dc:subject xml:lang="en-US">Artificial neural network; Deep learning; Output power; Prediction; Solar tracker</dc:subject>
	<dc:description xml:lang="en-US">The abundance of solar energy sources has encouraged many researchers to maximize solar photovoltaic (PV) output power using dual-axis solar tracking. However, environmental conditions, time of day, and the angle of movement of the solar tracker can affect the resulting power output. This study aims to predict the power output of dual-axis solar tracking in order to maintain the power’s stability and quality. Deep neural networks (DNN) with variations of 5 and 6 hidden layers have been proposed. The dataset used in this study was obtained from observation results and then divided into 80% training data and 20% testing data. A series of algorithms are used to recognize relationship patterns between input and hidden layers, between hidden layers, as well as hidden layers and output. Statistical results show that DNN with a variation of 6 hidden layers is better at estimating solar tracking power output with a mean absolute percentage error (MAPE) value of 12.328%, mean square error (MSE) of 0.332, and mean absolute error (MAE) of 0.425. This study can be used as a reference in utilizing artificial intelligence to predict the output power of solar panels as a renewable energy source.</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>2025-06-01</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://ijai.iaescore.com/index.php/IJAI/article/view/26629</dc:identifier>
	<dc:identifier>10.11591/ijai.v14.i3.pp2229-2235</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 14, No 3: June 2025; 2229-2235</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v14.i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/26629/14563</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>