Melola Discovery

← Melola Discovery

Open access Jurnal SINTA 1

Rule Optimization of Fuzzy Inference System Sugeno using Evolution Strategy for Electricity Consumption Forecasting

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 7 No. 4 (2017)

DOI
10.11591/ijece.v7i4.pp2241-2252
ISSN
2088-8708, 2088-8708
Published
2017-08-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

The need for accurate load forecasts will increase in the future because of the dramatic changes occurring in the electricity consumption. Sugeno fuzzy inference system (FIS) can be used for short-term load forecasting. However, challenges in the electrical load forecasting are the data used the data trend. Therefore, it is difficult to develop appropriate fuzzy rules for Sugeno FIS. This paper proposes Evolution Strategy method to determine appropriate rules for Sugeno FIS that have minimum forecasting error. Root Mean Square Error (RMSE) is used to evaluate the goodness of the forecasting result. The numerical experiments show the effectiveness of the proposed optimized Sugeno FIS for several test-case problems. The optimized Sugeno FIS produce lower RMSE comparable to those achieved by other well-known method in the literature.

Collaboration network of the authors

40 authors · 68 co-authorship links · 10 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • University of Brawijaya
  • State University of Malang
  • Universitas Merdeka Malang
  • Institut Teknologi Sepuluh Nopember
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Yusuf Priyo AnggodoUniversity of Brawijaya3
Asyrofa RahmiUniversity of Brawijaya2
Syaiful AnamUniversity of Brawijaya2
Adyan Nur AlfiyatinUniversity of Brawijaya2
Firnanda Al Islama Achyunda PutraUniversity of Brawijaya1
Fitri UtaminingrumUniversity of Brawijaya1
Ida WahyuniUniversity of Brawijaya1
Ervin YohannesUniversity of Brawijaya1
Hilman Nuril HadiUniversity of Brawijaya1
Rizal Setya PerdanaUniversity of Brawijaya1
Ghenniy RachmansyahUniversity of Brawijaya1
Ria FebriyanaUniversity of Brawijaya1
Vitara Nindya Putri HasanUniversity of Brawijaya1
Mohammad Zoqi SarwaniUniversity of Brawijaya1
Luthfi HidayatUniversity of Brawijaya1
Rifki Setya ArmandaUniversity of Brawijaya1
Garsinia Ely RianiUniversity of Brawijaya1
Fita Lathifatul Mu’asyarohAffiliation unknown1
Agus Wahyu WidodoUniversity of Brawijaya1
Diva KurnianingtyasUniversity of Brawijaya1
Muchammad Chandra Cahyo UtomoUniversity of Brawijaya1
Candra Fajri AnandaUniversity of Brawijaya1
Farhanna Mar’iUniversity of Brawijaya1
Cleoputri YusainyUniversity of Brawijaya1
Qoirul KotimahUniversity of Brawijaya1
Vivi Nur WijayaningrumUniversity of Brawijaya1
Aji Prasetya WibawaState University of Malang1
Nadia Roosmalita SariUniversity of Brawijaya1
Elta SonalithaUniversitas Merdeka Malang1
Gusti Ahmad Fanshuri AlfarisyUniversity of Brawijaya1
Muhammad Halim NatsirUniversity of Brawijaya1
Agung Mustika RizkiInstitut Teknologi Sepuluh Nopember1
Gusti Eka YuliastutiUniversity of Brawijaya1
Andi HamdianahUniversity of Brawijaya1
Hilman TaufiqUniversity of Brawijaya1
Aprilia Nur FauziyahUniversity of Brawijaya1
Amalia Kartika AriyaniUniversity of Brawijaya1

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.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Rule Optimization of Fuzzy Inference System Sugeno using Evolution Strategy for Electricity Consumption Forecasting</dc:title><dc:creator>Santika, Gayatri Dwi</dc:creator><dc:creator>Mahmudy, Wayan Firdaus</dc:creator><dc:creator>Naba, Agus</dc:creator><dc:description>&lt;jats:p&gt;The need for accurate load forecasts will increase in the future because of the dramatic changes occurring in the electricity consumption. Sugeno fuzzy inference system (FIS) can be used for short-term load forecasting. However, challenges in the electrical load forecasting are the data used the data trend. Therefore, it is difficult to develop appropriate fuzzy rules for Sugeno FIS. This paper proposes Evolution Strategy method to determine appropriate rules for Sugeno FIS that have minimum forecasting error. Root Mean Square Error (RMSE) is used to evaluate the goodness of the forecasting result. The numerical experiments show the effectiveness of the proposed optimized Sugeno FIS for several test-case problems. The optimized Sugeno FIS produce lower RMSE comparable to those achieved by other well-known method in the literature.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2017-08-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v7i4.pp2241-2252</dc:identifier><dc:identifier>10.11591/ijece.v7i4.pp2241-2252</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 7 No 4 (2017); 2241</dc:source><dc:source>2088-8708</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/7766/7251</dc:relation><dc:rights>http://creativecommons.org/licenses/by-nc/4.0</dc:rights></oai_dc:dc>