Short-Term Forecasting of Electricity Consumption Revenue on Java-Bali Electricity System using Jordan Recurrent Neural Network

Putri, Tesa Eranti; Firdaus, Aji Akbar; Sabilla, Wilda Imama

doi:10.20473/jisebi.4.2.96-105

Jurnal SINTA 1

Short-Term Forecasting of Electricity Consumption Revenue on Java-Bali Electricity System using Jordan Recurrent Neural Network

Journal of Information Systems Engineering and Business Intelligence · Vol. 4 No. 2 (2018) · pp. 96–105

DOI: 10.20473/jisebi.4.2.96-105
ISSN: 2443-2555, 2598-6333
Published: 2018-10-28
Publisher: Universitas Airlangga
Language: eng

Abstract

Depending on the day and time, electricity consumption tends to fluctuate and directly affects the amount of gained revenue for the company. To anticipate future economic change and to avoid losses in calculating the company's revenue, it is essential to forecast electricity consumption revenue as accurate as possible. In this paper, Jordan Recurrent Neural Network (JRNN) was used to do short term forecasting of the electricity consumption revenue from Java-Bali 500 kVA electricity system. Seven JRNN models were trained using electricity consumption revenue between January-March 2012 to predict the revenue of the first week of April 2012. As performance comparators, seven traditional feed forward Artificial Neural Network (ANN) models were also constructed. The forecasting results were as expected for both models, where both producing steady repeating pattern for weekdays, but failed quite poorly to predict the weekends' revenue. This suggests that in Indonesia, weekends' electricity consumption revenue has different characteristics than weekdays. Evaluation of the prediction result was carried out using Sum of Square Error (SSE) and Mean Square Error (MSE). The evaluation showed that JRNN produced smaller SSE and MSE values than traditional feed forward ANN, thus JRNN could predict the electricity consumption revenue of Java-Bali electricity system more accurately.

Keywords

Collaboration network of the authors

10 authors · 20 co-authorship links · 12 across different organisations

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Universitas Airlangga
Institut Teknologi Adhi Tama Surabaya
Institut Teknologi Sepuluh Nopember
Asian Institute of Technology
University of Brawijaya
State University of Malang
Affiliation unknown

Different organisations
Same organisation
Affiliation unknown
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Co-author	Organisation	Joint articles
Agus Zainal Arifin	Institut Teknologi Sepuluh Nopember	1
Sugiyanto Sugiyanto	Institut Teknologi Adhi Tama Surabaya	1
Nanang Fakhrur Rozi	Institut Teknologi Adhi Tama Surabaya	1
Yuita Arum Sari	University of Brawijaya	1
Nasa Zata Dina	Asian Institute of Technology	1
Syamsul Bachri	State University of Malang	1
Mochammad Nafkhan Alzamzami	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://e-journal.unair.ac.id/JISEBI/oai, first harvested 29 Okt 2026, last seen 1 Nov 2026.

Original metadata (oai_dc)

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	<dc:title xml:lang="en-US">Short-Term Forecasting of Electricity Consumption Revenue on Java-Bali Electricity System using Jordan Recurrent Neural Network</dc:title>
	<dc:creator>Putri, Tesa Eranti</dc:creator>
	<dc:creator>Firdaus, Aji Akbar</dc:creator>
	<dc:creator>Sabilla, Wilda Imama</dc:creator>
	<dc:subject xml:lang="en-US">Revenue forecasting</dc:subject>
	<dc:subject xml:lang="en-US">Electricity consumption</dc:subject>
	<dc:subject xml:lang="en-US">Java-Bali Electricity System</dc:subject>
	<dc:subject xml:lang="en-US">Jordan Recurrent Neural Network</dc:subject>
	<dc:description xml:lang="en-US">Depending on the day and time, electricity consumption tends to fluctuate and directly affects the amount of gained revenue for the company. To anticipate future economic change and to avoid losses in calculating the company's revenue, it is essential to forecast electricity consumption revenue as accurate as possible. In this paper, Jordan Recurrent Neural Network (JRNN) was used to do short term forecasting of the electricity consumption revenue from Java-Bali 500 kVA electricity system. Seven JRNN models were trained using electricity consumption revenue between January-March 2012 to predict the revenue of the first week of April 2012. As performance comparators, seven traditional feed forward Artificial Neural Network (ANN) models were also constructed. The forecasting results were as expected for both models, where both producing steady repeating pattern for weekdays, but failed quite poorly to predict the weekends' revenue. This suggests that in Indonesia, weekends' electricity consumption revenue has different characteristics than weekdays. Evaluation of the prediction result was carried out using Sum of Square Error (SSE) and Mean Square Error (MSE). The evaluation showed that JRNN produced smaller SSE and MSE values than traditional feed forward ANN, thus JRNN could predict the electricity consumption revenue of Java-Bali electricity system more accurately.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Airlangga</dc:publisher>
	<dc:date>2018-10-28</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://e-journal.unair.ac.id/JISEBI/article/view/9027</dc:identifier>
	<dc:identifier>10.20473/jisebi.4.2.96-105</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Information Systems Engineering and Business Intelligence; Vol. 4 No. 2 (2018): October; 96-105</dc:source>
	<dc:source>2443-2555</dc:source>
	<dc:source>2598-6333</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://e-journal.unair.ac.id/JISEBI/article/view/9027/5663</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2018 The Authors. Published by Universitas Airlangga.</dc:rights>
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