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  <Article>
    <Journal>
      <PublisherName>ijesm</PublisherName>
      <JournalTitle>International Journal of Engineering, Science and</JournalTitle>
      <PISSN>I</PISSN>
      <EISSN>S</EISSN>
      <Volume-Issue>volume 14,issue 9,</Volume-Issue>
      <PartNumber/>
      <IssueTopic>Multidisciplinary</IssueTopic>
      <IssueLanguage>English</IssueLanguage>
      <Season>September 2025</Season>
      <SpecialIssue>N</SpecialIssue>
      <SupplementaryIssue>N</SupplementaryIssue>
      <IssueOA>Y</IssueOA>
      <PubDate>
        <Year>2025</Year>
        <Month>09</Month>
        <Day>25</Day>
      </PubDate>
      <ArticleType>Engineering, Science and Mathematics</ArticleType>
      <ArticleTitle>Smart Grid Optimization Using IoT and AI for Load Management</ArticleTitle>
      <SubTitle/>
      <ArticleLanguage>English</ArticleLanguage>
      <ArticleOA>Y</ArticleOA>
      <FirstPage>19</FirstPage>
      <LastPage>27</LastPage>
      <AuthorList>
        <Author>
          <FirstName>Amit Kumar</FirstName>
          <LastName>Meshram</LastName>
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>N</CorrespondingAuthor>
          <ORCID/>
        </Author>
      </AuthorList>
      <DOI/>
      <Abstract>Smart grids combine modern information and communication technologies with power systems to deliver reliable, efficient, and flexible electricity. The integration of Internet of Things (IoT) sensors and devices with Artificial Intelligence (AI) algorithms enables fine-grained monitoring and intelligent decision-making for load management. This paper presents a comprehensive study of smart-grid load optimization using IoT for sensing and actuation and AI for forecasting, demand response, and real-time control. We review relevant literature, describe a system architecture, propose AI-driven optimization algorithms for demand-side and network-level control, present a simulation-based evaluation approach, discuss results and practical deployment considerations (privacy, security, scalability), and conclude with future research directions. The proposed methods demonstrate how coordinated IoT-enabled measurement and AI-based prediction/optimization reduce peak demand, flatten load curves, improve voltage/profile stability, and increase renewable integration—leading to operational cost savings and better grid resilience.</Abstract>
      <AbstractLanguage>English</AbstractLanguage>
      <Keywords/>
      <URLs>
        <Abstract>https://ijesm.co.in/ubijournal-v1copy/journals/abstract.php?article_id=15990&amp;title=Smart Grid Optimization Using IoT and AI for Load Management</Abstract>
      </URLs>
      <References>
        <ReferencesarticleTitle>References</ReferencesarticleTitle>
        <ReferencesfirstPage>16</ReferencesfirstPage>
        <ReferenceslastPage>19</ReferenceslastPage>
        <References/>
      </References>
    </Journal>
  </Article>
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