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  <doi_batch_id>aspg-18-1849-1792025447</doi_batch_id>
  <timestamp>20261015005047</timestamp>
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  <journal>
   <journal_metadata language="en">
    <full_title>Journal of Intelligent Systems and Internet of Things</full_title>
    <abbrev_title>JISIoT</abbrev_title>
    <issn media_type="print">2769-786X</issn>
    <issn media_type="electronic">2690-6791</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2023</year>
    </publication_date>
    <journal_volume>
     <volume>9</volume>
    </journal_volume>
    <issue>1</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Intelligent Load Identification of Household-Smart Meters Using Multilevel Decision Tree and Data Fusion Techniques</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Mohammed Hasan</given_name>
      <surname>Aldulaimi</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Computer Techniques Engineering, Al Mustaqbal University College , Babylon 51001, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Ibrahim</given_name>
      <surname>Najem</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Computer Techniques Engineering, Al-turath University College, Baghdad 10021, Ira; MEU Research Unit, Middle East University, Amman 11831, Jordan</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Tabarak Ali</given_name>
      <surname>Abdulhussein</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Accounting, College of Administrative and Financial Sciences, Imam Ja'afar Al-Sadiq University, Baghdad, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>M. H.</given_name>
      <surname>Ali</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Medical device technology Engineering, National University of Science and Technology, Thi Qar, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Asaad Shakir</given_name>
      <surname>Hameed</surname>
      <affiliations>
       <institution>
        <institution_name>Performance Quality Department, Mazaya University College, Thi-Qar, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>M.</given_name>
      <surname>Altaee</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Medical device technology Engineering, Alfarahidi University, Baghdad, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Hatira</given_name>
      <surname>Günerhan</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Mathematics, Faculty of Education, Kafkas University, Kars,</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>The DTA-LI system's fusion data method is crucial in the monitoring of appliance loads for the purposes of improving energy efficiency and management. Common home electrical devices are identified and classified from smart meter data through the analysis of voltage and current variations, allowing for the measurement of energy usage in residential buildings. A load identification system based on a decision tree algorithm may infer information about the residents of a building based on their energy usage habits. Better power savings rates, load shedding management, and overall electrical system performance are the results of the clusters' ability to capture families' purchasing patterns and geo-Demographic segmentation. The DTA-LI system's fusion data method presents a promising avenue for improving residential buildings' energy performance and lowering their carbon footprint, especially in light of the widespread use of smart meters in recent years.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2023</year>
    </publication_date>
    <pages>
     <first_page>24</first_page>
     <last_page>35</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">1849</item_number>
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     <ai:license_ref applies_to="vor">https://creativecommons.org/licenses/by/4.0/</ai:license_ref>
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     <doi>10.54216/JISIoT.090102</doi>
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