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Performance Evaluation of NARX-CG Model for Electricity Forecasting: Bali Blackout Case Study

Jurnal Teknik Elektro · Vol. 17 No. 2 (2024) · hlm. 64–69

DOI
10.15294/jte.v17i2.35519
ISSN
2549-1571, 1411-0059
Terbit
2024-12-30
Penerbit
LPPM Universitas Negeri Semarang
Bahasa
eng

Abstrak

Bali experienced a widespread blackout in May 2025 that disrupted economic and social activities across the island, revealing weaknesses in electricity demand forecasting and system resilience. This study evaluates the performance of a Hybrid Nonlinear Autoregressive with Exogenous Inputs-Conjugate Gradient (NARX-CG) model as an advanced electricity forecast. The dataset covers the 2018-2023 period and includes six variables: electricity energy, connected capacity, number of customers, tariffs, Gross Regional Domestic Product (GRDP), and population, aligned with the national electricity planning framework. The NARX-CG model was developed using a 6-12-6-1 network architecture and trained with tansig transfer function. Forecasting performance was evaluated using Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) metrics. Results show that the NARX-CG model achieved an MSE of 0.09853 and an average MAPE of 8.12%, outperforming conventional projections with a MAPE of 28.48%. Yearly evaluations show consistent model stability, with the lowest MAPE values of 1.93% and 5.86% in 2023 and 2022, respectively. The NARX-CG model effectively captures nonlinear temporal dependencies, enhances predictive accuracy, and contributes to improved power system reliability and resilience, providing valuable insights for adaptive energy planning following the 2025 Bali blackout.

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7 penulis · 12 tautan kepenulisan bersama · 5 lintas organisasi

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  • Universitas Islam Negeri Sultan Syarif Kasim Riau
  • Universitas Lancang Kuning
  • National University of Malaysia
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Rekan penulisOrganisasiArtikel bersama
Muhammad Guido Randa Febiant GuidoUniversitas Islam Negeri Sultan Syarif Kasim Riau2
LilianaUniversitas Islam Negeri Sultan Syarif Kasim Riau2
Ahmad FudholiNational University of Malaysia1
Zainal AhnurUniversitas Islam Negeri Sultan Syarif Kasim Riau1
David SetiawanUniversitas Lancang Kuning1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/jte/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">Performance Evaluation of NARX-CG Model for Electricity Forecasting: Bali Blackout Case Study</dc:title>
	<dc:creator xml:lang="en">Alaqsa, Tengku Reza Suka</dc:creator>
	<dc:creator xml:lang="en">Aini, Zulfatri</dc:creator>
	<dc:subject xml:lang="en">Bali blackout</dc:subject>
	<dc:subject xml:lang="en">conjugate gradient</dc:subject>
	<dc:subject xml:lang="en">forecasting</dc:subject>
	<dc:subject xml:lang="en">NARX</dc:subject>
	<dc:subject xml:lang="en">performance evaluation</dc:subject>
	<dc:description xml:lang="en">Bali experienced a widespread blackout in May 2025 that disrupted economic and social activities across the island, revealing weaknesses in electricity demand forecasting and system resilience. This study evaluates the performance of a Hybrid Nonlinear Autoregressive with Exogenous Inputs-Conjugate Gradient (NARX-CG) model as an advanced electricity forecast. The dataset covers the 2018-2023 period and includes six variables: electricity energy, connected capacity, number of customers, tariffs, Gross Regional Domestic Product (GRDP), and population, aligned with the national electricity planning framework. The NARX-CG model was developed using a 6-12-6-1 network architecture and trained with tansig transfer function. Forecasting performance was evaluated using Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) metrics. Results show that the NARX-CG model achieved an MSE of 0.09853 and an average MAPE of 8.12%, outperforming conventional projections with a MAPE of 28.48%. Yearly evaluations show consistent model stability, with the lowest MAPE values of 1.93% and 5.86% in 2023 and 2022, respectively. The NARX-CG model effectively captures nonlinear temporal dependencies, enhances predictive accuracy, and contributes to improved power system reliability and resilience, providing valuable insights for adaptive energy planning following the 2025 Bali blackout.</dc:description>
	<dc:publisher xml:lang="en">LPPM Universitas Negeri Semarang</dc:publisher>
	<dc:date>2024-12-30</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">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/jte/article/view/35519</dc:identifier>
	<dc:identifier>10.15294/jte.v17i2.35519</dc:identifier>
	<dc:source xml:lang="en">Jurnal Teknik Elektro; Vol. 17 No. 2 (2025); 64-69</dc:source>
	<dc:source>2549-1571</dc:source>
	<dc:source>1411-0059</dc:source>
	<dc:source>10.15294/jte.v17i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/jte/article/view/35519/7919</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 Jurnal Teknik Elektro</dc:rights>
</oai_dc:dc>