Electric Load Forecasting in Maros City Based on Extreme Learning Machine (ELM)

Muhammad Zhahran Zhafirin Irawan; Yusri Syam Akil; Indar Chaerah Gunadin; Muhammad Zhahran Zhafirin Irawan; Yusri Syam Akil; Indar Chaerah Gunadin

Jurnal

Electric Load Forecasting in Maros City Based on Extreme Learning Machine (ELM)

Peramalan Beban Listrik Kota Maros Berbasis Extreme Learning Machine (ELM)

Jurnal Eksitasi · Vol. 1 No. 2 (2022) · pp. 22–28

ISSN: 2829-5110
Published: 2022-11-14
Publisher: Departemen Teknik Elektro
Language: id

Abstract

English

Electrical load forecasting is one way to reduce the risk of unstable electricity supply. In writing this journal, electrical load forecasting is carried out using an artificial neural network using the Extreme Learning Machine (ELM) method. Extreme Learning Machine (ELM) is a new learning method in artificial neural networks with a single layer feedforward neural network model. The accuracy of the Extreme Learning Machine (ELM) method can be calculated using the Mean Absolute Percentage Error (MAPE). Based on the implementation carried out From the electricity load data for the city of Maros, it is known that of the two extreme learning machine activation functions that were simulated (linear and logsig), the linear activation function provides better daily electrical load forecasting results with a MAPE value of 5.44%.

Bahasa Indonesia

Prakiraan beban listrik merupakan salah satu cara mengurangi resiko penyediaan tenaga listrik yang tidak stabil.Pada penulisan jurnal ini prakiraan beban listrik dilakukan dengan menggunakan jaringan syaraf tiruan metode Extreme Learning Machine (ELM). Extreme Learning Machine (ELM) adalah sebuah metode pembelajaran baru dalam jaringan syaraf tiruan dengan model single layer feedforward neural networks.Keakuratan dari metode Extreme Learning Machine (ELM) ini dapat di hitung dengan menggunakan Mean Absolute Percentage Error (MAPE).Berdasarkan implementasi yang dilakukan pada data beban listrik kota Maros diketahui bahwa dari dua fungsi aktivasi extreme learning machine yang di simulasi (linear dan logsig), fungsi aktivasi linear memberikan hasil prakiraan beban listrik harian yang lebih baik dengan nilai MAPE sebesar 5,44%

Keywords

Collaboration network of the authors

17 authors · 27 co-authorship links

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Co-author	Organisation	Joint articles
Zaenab Muslimin	Affiliation unknown	3
Yustinus Upa Sombolayuk	Affiliation unknown	2
Harits Ghazy Ramadhan	Affiliation unknown	2
Muammar Fadli Nur	Affiliation unknown	1
Fikran Fauzy	Affiliation unknown	1
Intan Sari Areni	Affiliation unknown	1
Muh. Rafiud Priatna Yusrifal	Affiliation unknown	1
Yusran	Affiliation unknown	1
Muhammad Irsan Sadri	Affiliation unknown	1
Abd Rahim	Affiliation unknown	1
Firda Fajryani Kamil	Affiliation unknown	1
Caesar William Alexander	Affiliation unknown	1
Sri Mawar Said	Affiliation unknown	1
Fatimah Az-zahra	Affiliation unknown	1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/eksitasi/oai, first harvested 2 Nov 2026, last seen 2 Nov 2026.

Original metadata (oai_dc)

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	<dc:title xml:lang="en">Electric Load Forecasting in Maros City Based on Extreme Learning Machine (ELM)</dc:title>
	<dc:title xml:lang="id">Peramalan Beban Listrik Kota Maros Berbasis Extreme Learning Machine (ELM) </dc:title>
	<dc:creator xml:lang="en">Muhammad Zhahran Zhafirin Irawan</dc:creator>
	<dc:creator xml:lang="en">Yusri Syam Akil </dc:creator>
	<dc:creator xml:lang="en">Indar Chaerah Gunadin</dc:creator>
	<dc:creator xml:lang="id">Muhammad Zhahran Zhafirin Irawan</dc:creator>
	<dc:creator xml:lang="id">Yusri Syam Akil </dc:creator>
	<dc:creator xml:lang="id">Indar Chaerah Gunadin</dc:creator>
	<dc:subject xml:lang="en">Extreme Learning Machine</dc:subject>
	<dc:subject xml:lang="en">Network Artificial Nerve</dc:subject>
	<dc:subject xml:lang="en">Electrical Load</dc:subject>
	<dc:subject xml:lang="en">Load Forecast Electricity</dc:subject>
	<dc:subject xml:lang="id">Extreme Learning Machine</dc:subject>
	<dc:subject xml:lang="id">Jaringan Syaraf Tiruan</dc:subject>
	<dc:subject xml:lang="id">Beban Listrik</dc:subject>
	<dc:subject xml:lang="id">Prakiraan Beban Listrik</dc:subject>
	<dc:description xml:lang="en">Electrical load forecasting is one way to reduce the risk of unstable electricity supply. In writing this journal, electrical load forecasting is carried out using an artificial neural network using the Extreme Learning Machine (ELM) method. Extreme Learning Machine (ELM) is a new learning method in artificial neural networks with a single layer feedforward neural network model. The accuracy of the Extreme Learning Machine (ELM) method can be calculated using the Mean Absolute Percentage Error (MAPE). Based on the implementation carried out From the electricity load data for the city of Maros, it is known that of the two extreme learning machine activation functions that were simulated (linear and logsig), the linear activation function provides better daily electrical load forecasting results with a MAPE value of 5.44%.</dc:description>
	<dc:description xml:lang="id">Prakiraan beban listrik merupakan salah satu cara mengurangi resiko penyediaan tenaga listrik yang tidak stabil.Pada penulisan jurnal ini prakiraan beban listrik dilakukan dengan menggunakan jaringan syaraf tiruan metode Extreme Learning Machine (ELM). Extreme Learning Machine (ELM) adalah sebuah metode pembelajaran baru dalam jaringan syaraf tiruan dengan model single layer feedforward neural networks.Keakuratan dari metode Extreme Learning Machine (ELM) ini dapat di hitung dengan menggunakan Mean Absolute Percentage Error (MAPE).Berdasarkan implementasi yang dilakukan pada data beban listrik kota Maros diketahui bahwa dari dua fungsi aktivasi extreme learning machine yang di simulasi (linear dan logsig), fungsi aktivasi linear memberikan hasil prakiraan beban listrik harian yang lebih baik dengan nilai MAPE sebesar 5,44%</dc:description>
	<dc:publisher xml:lang="id">Departemen Teknik Elektro</dc:publisher>
	<dc:date>2022-11-14</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="id">Artikel Peer-review</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unhas.ac.id/index.php/eksitasi/article/view/24009</dc:identifier>
	<dc:source xml:lang="en">Jurnal Eksitasi; Vol. 1 No. 2 (2022): Journal of Excitation of the Department of Electrical Engineering. Vol. 1. No. 2. 2022; 22-28</dc:source>
	<dc:source xml:lang="id">JURNAL EKSITASI DEPARTEMEN TEKNIK ELEKTRO; Vol 1 No 2 (2022): Jurnal Eksitasi Departemen Teknik Elektro Vol. 1. No. 2.  2022; 22-28</dc:source>
	<dc:source>2829-5110</dc:source>
	<dc:language>id</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/eksitasi/article/view/24009/8862</dc:relation>
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