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COMPARATIVE ANALYSIS OF FUZZY TIME SERIES CHEN AND MARKOV CHAIN METHODS FOR FORECASTING ELECTRICITY CONSUMPTION IN MATARAM CITY

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 18 No. 4 (2024) · pp. 2375–2386

DOI
10.30598/barekengvol18iss4pp2375-2386
ISSN
2615-3017, 1978-7227
Published
2024-10-11
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The consumption of electrical energy continues to experience fluctuations every month, and these fluctuations cannot be accurately predicted. This uncertainty can become a problem if not projected and planned effectively. Therefore, PT PLN (Persero) needs to be able to provide and distribute electricity supply in an appropriate amount. This research aims to forecast electricity consumption based on historical data from January 2016 to April 2023 using the Fuzzy Time Series Chen (FTSC) method and the Fuzzy Time Series Markov Chain (FTSMC) method. The results of this research show that the forecast for May 2023 using the FTSC and FTSMC methods are 136.878.489 kWh and 143.498.523 kWh, respectively, with MAPE values of 11.61739% and 4.85428%, respectively. Therefore, forecasting in May 2023 using the FTSMC method is better than the FTSC method because the MAPE value is smaller.

Collaboration network of the authors

40 authors · 106 co-authorship links · showing the 40 most connected

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  • State University of Medan
  • Satya Wacana Christian University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Musrini MuisAffiliation unknown2
La Onu La OlaAffiliation unknown1
Utama K. PangerangAffiliation unknown1
Sitti Wahidah MasnaniAffiliation unknown1
Tirsa AminahAffiliation unknown1
Fauziah Khairani LubisState University of Medan1
Adi SetiawanSatya Wacana Christian University1
Khairul AnsharAffiliation unknown1
Zainuddin GintingAffiliation unknown1
Lailia AwalushaumiAffiliation unknown1
Bulqis Nebulla SyechahAffiliation unknown1
Satriawan PradanaAffiliation unknown1
septia amrylianaAffiliation unknown1
Muhammad Rijal AlfianAffiliation unknown1
Nuzla Af’idatur RobbaniyyahAffiliation unknown1
Ismi AsmawatiAffiliation unknown1
Andi Lukman IrwanAffiliation unknown1
Nurlinah NurlinahAffiliation unknown1
Al MusannaAffiliation unknown1
Andi SantiAffiliation unknown1
Hamidin RasuluAffiliation unknown1
Janiah HusenAffiliation unknown1
Angela WulansariAffiliation unknown1
Ikrima M. Mustafa MustafaAffiliation unknown1
Asep NurhikmatAffiliation unknown1
Nancy KiayAffiliation unknown1
Nurjanna AlbaarAffiliation unknown1
Mila FatmawatiAffiliation unknown1
Nabel Ahmed A MansourAffiliation unknown1
Tirta WiryaAffiliation unknown1
Gando SariAffiliation unknown1
Nursama HeruAffiliation unknown1
Resa Wantika UtamiAffiliation unknown1
Burhan DjamaluddinAffiliation unknown1
Nurlailah NurlailahAffiliation unknown1
Ahmad ZamzamyAffiliation unknown1
Ika Kana TrisnawatiAffiliation unknown1

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://ojs3.unpatti.ac.id/index.php/barekeng/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (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">COMPARATIVE ANALYSIS OF FUZZY TIME SERIES CHEN AND MARKOV CHAIN METHODS FOR FORECASTING ELECTRICITY CONSUMPTION IN MATARAM CITY</dc:title>
	<dc:creator>Nirwanto, Nirwanto</dc:creator>
	<dc:creator>Bahri, Syamsul</dc:creator>
	<dc:creator>Harsyiah, Lisa</dc:creator>
	<dc:description xml:lang="en-US">The consumption of electrical energy continues to experience fluctuations every month, and these fluctuations cannot be accurately predicted. This uncertainty can become a problem if not projected and planned effectively. Therefore, PT PLN (Persero) needs to be able to provide and distribute electricity supply in an appropriate amount. This research aims to forecast electricity consumption based on historical data from January 2016 to April 2023 using the Fuzzy Time Series Chen (FTSC) method and the Fuzzy Time Series Markov Chain (FTSMC) method. The results of this research show that the forecast for May 2023 using the FTSC and FTSMC methods are 136.878.489 kWh and 143.498.523 kWh, respectively, with MAPE values of 11.61739% and 4.85428%, respectively. Therefore, forecasting in May 2023 using the FTSMC method is better than the FTSC method because the MAPE value is smaller.
&amp;nbsp;</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2024-10-11</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://ojs3.unpatti.ac.id/index.php/barekeng/article/view/12802</dc:identifier>
	<dc:identifier>10.30598/barekengvol18iss4pp2375-2386</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 18 No 4 (2024): BAREKENG: Journal of Mathematics and Its Application; 2375-2386</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol18iss4year2024</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/12802/8846</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Nirwanto Nirwanto, Syamsul Bahri, Lisa Harsyiah</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>