[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121475-en":3,"doc-seo-121475-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121475,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","A reliable and secure demand side control for an IoT-enabled smart power system using machine learning - Research article","As IoT-enabled smart power systems expand, dependable and secure demand-side control becomes essential for stable operation under rising cyber risk. This paper proposes a robust DSM engine that uses machine learning for energy-demand forecasting, anomaly detection, and adaptive control to prevent overloads while improving load balancing. The approach integrates IoT devices for real-time data acquisition and includes security protocols to protect integrity and privacy against cyber threats. Simulation results show reduced energy wastage and improved grid reliability.","Indonesian Journal of Electrical Engineering and Computer Science  \nVol. 37, No. 3, March 2025, pp. 1428~ 1434  \nISSN: 2502-4752, DOI: 10. 11591/ijeecs.v37 . i3 .pp1428-1434 􀂈 1428  \n\n| A reliable and secure demand side control for an IoT-enabled smart power system using machine learning\u003Cbr>Vemulapalli Harika1, Gudavalli Madhavi1, Hanumantha Rao Battu2, Rambabu Kasukurthi3,\u003Cbr>Pradeep Jangir4,5, John T Mesia Dhas6\u003Cbr>1Department of Electrical and Electronics Engineering, Prasad V Potluri Siddhartha Institute of Technology, Vijayawada, India 2Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation (KLEF), Guntur, India 3Department of Electrical and Electronics Engineering, Aditya University, Peddapuram, India 4Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India 5Applied Science Research Center, Applied Science Private University, Amman, Jordan\u003Cbr>6Department of Computer Science and Engineering, School of Computing, Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India |  |\n| --- | --- |\n| Article Info\u003Cbr>Article history:\u003Cbr>Received Aug 3, 2024 Revised Sep 27, 2024 Accepted Oct 7, 2024\u003Cbr>Keywords:\u003Cbr>Cybersecurity\u003Cbr>Demand side management Energy efficiency\u003Cbr>IoT\u003Cbr>Machine learning\u003Cbr>Smart power system\u003Cbr>Corresponding Author: | As the adoption of IoT-enabled smart power systems grows, the necessity for reliable and secure demand-side control becomes paramount. This paper introduces a robust demand-side management (DSM) engine that leverages machine learning to enhance both the reliability and security of smart grids. This paper presents a novel demand-side control system leveraging advanced machine learning techniques to optimize energy usage in smart power systems. The proposed system integrates IoT devices for data acquisition and employs machine learning algorithms to forecast energy demand, detect anomalies, and enable adaptive control strategies. By harnessing predictive analytics, the system anticipates consumption patterns and adjusts power distribution to maintain stability and prevent overloads. Moreover, robust security protocols are incorporated to protect the system against cyber threats and unauthorized access, ensuring data integrity and user privacy. Extensive simulation results demonstrate the system ’s efficacy in reducing energy wastage, improving load balancing, and enhancing the overall reliability of the power grid. This research underscores the potential of combining IoT and machine learning to develop resilient and secure energy management solutions, paving the way for more sustainable and smart power systems.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr>\u003Cbr>ABSTRACT |\n| Vemulapalli Harika\u003Cbr>Department of Electrical and Electronics Engineering, Prasad V Potluri Siddhartha Institute of Technology Vijayawada, India\u003Cbr>Email: [vemulapalliharika2312@gmail.com](vemulapalliharika2312@gmail.com) |  |\n\n1. INTRODUCTION  \nThe incorporation of internet of things (IoT) technology in intelligent power systems has transformed energy management and consumption. The IoT enables real-time monitoring and control, hence facilitating efficient demand-side management (DSM), essential for balancing supply and demand in power systems [1], [2] . The growing interconnectedness also creates weaknesses that cyber attackers can exploit, presenting substantial concerns to the dependability and security of the power grid [3] . Machine learning provides effective solutions to improve DSM by forecasting consumption trends and optimizing energy utilization. When integrated with stringent security standards, machine learning can substantially reduce the  \nrisks linked to cyber-attacks. This study offers a thorough methodology for DSM that combines machine learning with cybersecurity strategies to establish a dependable and secure smart power system [4], [5] .  \nDSM uses ma","cbCaigwU3zuGE0od","https://ap.wps.com/l/cbCaigwU3zuGE0od","pdf",374328,1,7,"English","en",105,"# Abstract\n# Introduction\n## IoT-enabled DSM and reliability needs\n## Cybersecurity threats in smart grids\n## Machine learning methods for demand forecasting and control\n## Security techniques (encryption, IDS, blockchain)\n# Conclusion","[{\"question\":\"What problem does the paper address in IoT-enabled smart power systems?\",\"answer\":\"It focuses on providing reliable and secure demand-side control as IoT adoption increases. The work targets both operational stability (supply-demand balance) and protection against cyber threats.\"},{\"question\":\"How does the proposed DSM engine use machine learning?\",\"answer\":\"It leverages machine learning to forecast energy demand, detect anomalies, and support adaptive control strategies. Predictive analytics help anticipate consumption patterns and adjust power distribution accordingly.\"},{\"question\":\"What security measures are incorporated to protect the smart power system?\",\"answer\":\"The system includes robust security protocols to guard against cyber threats and unauthorized access. These measures aim to ensure data integrity and user privacy.\"}]","A reliable and secure demand side control for an IoT-enabled smart power system using machine learning - Research article | PDF",1785735825,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-reliable-and-secure-demand-side-control-for-an-iot-enabled-smart-power-system-using-machine-learning-research-article","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-reliable-and-secure-demand-side-control-for-an-iot-enabled-smart-power-system-using-machine-learning-research-article/121475/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address in IoT-enabled smart power systems?","Question",{"text":75,"@type":76},"It focuses on providing reliable and secure demand-side control as IoT adoption increases. The work targets both operational stability (supply-demand balance) and protection against cyber threats.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed DSM engine use machine learning?",{"text":80,"@type":76},"It leverages machine learning to forecast energy demand, detect anomalies, and support adaptive control strategies. Predictive analytics help anticipate consumption patterns and adjust power distribution accordingly.",{"name":82,"@type":73,"acceptedAnswer":83},"What security measures are incorporated to protect the smart power system?",{"text":84,"@type":76},"The system includes robust security protocols to guard against cyber threats and unauthorized access. 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