Publication:
Data mining and analysis for predicting electrical energy consumption

Simple item page
dc.citedby3
dc.contributor.authorKhudhair I.Y.en_US
dc.contributor.authorDhahi S.H.en_US
dc.contributor.authorAlwan O.F.en_US
dc.contributor.authorJaaz Z.A.en_US
dc.contributor.authorid57202320194en_US
dc.contributor.authorid57220866637en_US
dc.contributor.authorid58017682000en_US
dc.contributor.authorid57210340202en_US
dc.date.accessioned2024-10-14T03:18:38Z
dc.date.available2024-10-14T03:18:38Z
dc.date.issued2023
dc.description.abstractIn this study paper, the feasibility of constructing a complete smart system for anticipating electrical power consumption is created, as electricity's market share is expected to expand over the future decades. Smart grids and smart meters will help utility companies and their customers soon. New services and businesses in energy management need software development and data analytics skills. New services and enterprises are competitive. The project's electricity consumers are categorized by their hourly power usage percentage. This classification was done using data mining (five algorithms in specific) and data analysis theory. This division aims to help each group minimize energy use and expenditures, encourage energy-saving activities, and promote consumer involvement by giving tailored guidance. The intended segmentation is done through an iterative process using a computer classification computation, post-analysis, and data mining with visualization and statistical methodologies. � 2023, Institute of Advanced Engineering and Science. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11591/eei.v12i2.4593
dc.identifier.epage1006
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85144038216
dc.identifier.spage997
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85144038216&doi=10.11591%2feei.v12i2.4593&partnerID=40&md5=fffa343e25504e2412634cb92b9c948d
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34248
dc.identifier.volume12
dc.pagecount9
dc.publisherInstitute of Advanced Engineering and Scienceen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.relation.ispartofGreen Open Access
dc.sourceScopus
dc.sourcetitleBulletin of Electrical Engineering and Informatics
dc.subjectClustering
dc.subjectData analysis
dc.subjectData mining
dc.subjectElectric usage
dc.subjectElectricity consumption
dc.subjectPrediction
dc.titleData mining and analysis for predicting electrical energy consumptionen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS