Melola Discovery

← Melola Discovery

Open access Journal SINTA 1

Short-term load forecasting of the distribution system using cuckoo search algorithm

International Journal of Power Electronics and Drive Systems · Vol. 13 No. 1 (2022) · pp. 159–166

DOI
10.11591/ijpeds.v13.i1.pp159-166
ISSN
2722-256X, 2088-8694
Published
2022-03-01
Publisher
Institute of Advanced Engineering and Science
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

For solving the different optimization problems, the cuckoo search is one of the best nature's inspired algorithms. It is an effective technique compare to other optimization methods. For this manuscript, we are using a back propagation neural network for the Xintai power plant consist of short-term electrical load forecasting. The limitation of back propagation is overcome by the cuckoo search algorithm. The function is used for cuckoo search is Gamma probability distribution and its result is compared with other possible cuckoo search methods. The mean average percentage error of Gamma cuckoo search is 0.123%, cuckoo search with Pareto based is 0.127% and Levy based cuckoo search is 0.407%. Other results of the cuckoo search are also found by a linear decreasing switching parameter with a mean average error is 0.344% and 0.389% of mean average error with the use of an exponentially increasing switching parameter. This improved cuckoo search algorithm brings good results in the predicted load which is very important for the Xintai power plant using short-term load forecasting.

Keywords

Collaboration network of the authors

40 authors · 111 co-authorship links · 4 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.

  • Woosong University
  • Veer Surendra Sai University of Technology
  • Biju Patnaik University of Technology
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Rudranarayan SenapatiAffiliation unknown10
Sivkumar MishraBiju Patnaik University of Technology6
Seong-Cheol KimWoosong University5
Sanchita SahayAffiliation unknown4
Ayush KumarAffiliation unknown4
Arun Kumar SahooAffiliation unknown4
S. NarasimhaAffiliation unknown3
Chan-Mook JungWoosong University2
Aditya Prasad MahapatraAffiliation unknown2
Bishweashwar SuklaAffiliation unknown2
Harikrishnan K. M.Affiliation unknown2
Padarabinda PalaiAffiliation unknown2
Nisha KediaAffiliation unknown2
Raj Alpha SwainAffiliation unknown2
Sandip Ranjan BeheraAffiliation unknown2
Arul Kumar DashAffiliation unknown2
Prajna Jeet OjhaAffiliation unknown2
Lingam YashwanthAffiliation unknown2
Peesodi UdayAffiliation unknown2
Bhabesh RoutrayAffiliation unknown2
Nitesh PattanayakAffiliation unknown2
Priyansu ShekharAffiliation unknown2
Pratyus Ranjan BeheraAffiliation unknown2
Priyanka raiAffiliation unknown2
Prince KumarAffiliation unknown2
Prabin BiswalAffiliation unknown2
Swayamjyoti SatapathyAffiliation unknown2
Smruti Susmita SahuAffiliation unknown2
Jayanta Kumar SahuAffiliation unknown2
Umamani SubudhiAffiliation unknown2
Drishana JhunjhunwallaAffiliation unknown2
Pankaj SharmaWoosong University1
Monalisa PandaAffiliation unknown1
Debani Prashad MishraAffiliation unknown1
Sopa Mousumi PatroAffiliation unknown1
Kalpa Ranjan BeheraAffiliation 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) http://ijpeds.iaescore.com/index.php/IJPEDS/oai, first harvested 8 Nov 2026, last seen 8 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">Short-term load forecasting of the distribution system using cuckoo search algorithm</dc:title>
	<dc:creator>Panda, Saroj Kumar</dc:creator>
	<dc:creator>Ray, Papia</dc:creator>
	<dc:creator>Mishra, Debani Prasad</dc:creator>
	<dc:creator>Salkuti, Surender Reddy</dc:creator>
	<dc:subject xml:lang="en-US">artificial neural network; back propagation; cuckoo search; distribution system; optimization; short term load forecasting</dc:subject>
	<dc:description xml:lang="en-US">For solving the different optimization problems, the cuckoo search is one of the best nature's inspired algorithms. It is an effective technique compare to other optimization methods. For this manuscript, we are using a back propagation neural network for the Xintai power plant consist of short-term electrical load forecasting. The limitation of back propagation is overcome by the cuckoo search algorithm. The function is used for cuckoo search is Gamma probability distribution and its result is compared with other possible cuckoo search methods. The mean average percentage error of Gamma cuckoo search is 0.123%, cuckoo search with Pareto based is 0.127% and Levy based cuckoo search is 0.407%. Other results of the cuckoo search are also found by a linear decreasing switching parameter with a mean average error is 0.344% and 0.389% of mean average error with the use of an exponentially increasing switching parameter. This improved cuckoo search algorithm brings good results in the predicted load which is very important for the Xintai power plant using short-term load forecasting.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2022-03-01</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-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijpeds.iaescore.com/index.php/IJPEDS/article/view/21171</dc:identifier>
	<dc:identifier>10.11591/ijpeds.v13.i1.pp159-166</dc:identifier>
	<dc:source xml:lang="en-US">International Journal of Power Electronics and Drive Systems (IJPEDS); Vol 13, No 1: March 2022; 159-166</dc:source>
	<dc:source>2722-256X</dc:source>
	<dc:source>2088-8694</dc:source>
	<dc:source>10.11591/ijpeds.v13.i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijpeds.iaescore.com/index.php/IJPEDS/article/view/21171/13727</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Saroj Kumar Panda, Papia Ray, Debani Prasad Mishra, Surender Reddy Salkuti</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>