[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120553-en":3,"doc-seo-120553-105":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},120553,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Adaptive Machine Learning Techniques for Enhancing Smart Grid Data Integrity - Article","Ensuring data integrity in smart power grids is essential for optimized operation and planning, yet rising renewable penetration and flexible loads introduce uncertainty and complex spatiotemporal data patterns. Centralized learning faces barriers including data privacy exposure, communication overhead, and limited adaptiveness to changing grid conditions. The paper introduces adaptive machine-learning methods using distributed local training and parameter sharing to build an aggregated global model without exchanging raw private data. Edge-based decentralized training improves personalization and accuracy, enabling station-specific adaptive forecasts and strengthening smart microgrid resilience while supporting capacity planning and retail pricing for future sustainable grids.","International Journal of Computations, Information and Manufacturing (IJCIM ) 4 (1) -2024  \n\n|  | \u003Cbr>Contents available at the publisher website: G A F T I M . C O M\u003Cbr>\u003Cbr>\u003Cbr>Journal homepage: [https://journals.gaftim.com/index.php/ijcim/index](https://journals.gaftim.com/index.php/ijcim/index)\u003Cbr> |  |\n| --- | --- | --- |\n| Adaptive Machine Learning Techniques for Enhancing Smart Grid Data Integrity Ibar Federico Anderson\u003Cbr>School of Economics and Management, National University of La Plata (UNLP), Argentine |  |  |\n| A R T I C L E I N F O\u003Cbr>Keywords:\u003Cbr>Smart Grid, Machine Learning, Load Forecasting, Smart City, Energy\u003Cbr>Received: Feb, 19, 2024\u003Cbr>Accepted: Apr, 10, 2024\u003Cbr>Published: Jun, 22, 2024 |  | A B S T R A C T\u003Cbr>Ensuring data integrity in smart power grids is crucial for their optimized operation and planning. However, the increasing penetration of renewable energy sources and the emergence of flexible loads like electric vehicles create significant uncertainties and complexities in data patterns. Traditional centralized models struggle with data privacy concerns, communication overheads, and lack of model adaptiveness. This paper proposes adaptive machine-learning techniques for enhancing data integrity in smart grids. Local machine learning models are trained on distributed private datasets across different stations of the grid, and only the model parameters are communicated to a central server to create an aggregated global model, without exchanging any raw private data. The proposed approach harnesses edge resources efficiently through decentralized on-device training while providing enhanced accuracy and personalization over centralized models. Several experiments conducted on electricity consumption data validate the effectiveness of our approach in handling complex spatiotemporal changes and generating station-specific adaptive forecasts. By adopting a decentralized approach, our methodology seeks to enhance grid resilience by preserving data privacy, mitigating security risks, and optimizing the efficiency of smart microgrid operations. The proposed solution can enable optimized capacity planning and retail pricing for sustainable grids ofthe future. |\n\n1. INTRODUCTION  \nSmart cities represent a thrilling new era of urban development and innovation, aiming to elevate civic infrastructure and services through cuttingedge technologies. Intelligent mobility systems reduce congestion, while smart grids power homes and offices sustainably [1],[2],[3] . Advanced data analytics and IoT applications improve citizens'efficiency, equity, and quality of life. The challenge remains in efficiently coordinating the many complex, interdependent systems that hold up metropolitan infrastructure, however. In making sure that the full potential of smart city subsystems, such as energy, water, and waste management, reaches people, these have to be optimized. One of the most critical factors is the electrical grid; nearly all smart city functions  \ncannot operate without this [4], [5], [6] . Data integrity in smart grids is one of the most important requirements for reliable operations and security. With their wide scope and reach of applications, smart grid deployment has continued to lag due to several challenges, not least of which include data privacy concerns, high communication overheads, and adaptive models in managing dynamic conditions. In this regard, adaptive machine learning techniques can help in enhancing data integrity and reducing security risks within smart grids and optimizing their operation for more resilient and efficient smart cities [7], [8] .  \nSmart grids serve as the backbones of electricity infrastructures today, wherein advanced metering,  \ncontrol, and coordination work together to achieve optimized power generation, distribution, and consumption. Bidirectional energy and data flows provide enhanced visibility and control between utilities and end users. However, several critical issues stand ","cbCaidIpEuUDzgm7","https://ap.wps.com/l/cbCaidIpEuUDzgm7","pdf",481372,1,7,"English","en",105,"# Introduction\n## Motivation for data integrity in smart grids\n## Limitations of centralized and traditional models\n# Proposed adaptive machine-learning approach\n## Decentralized edge training and privacy preservation\n## Aggregated global model without raw data sharing\n# Experimental validation\n## Electricity consumption data and station-specific forecasts\n# Impact and application\n## Grid resilience, efficiency, capacity planning, and pricing","[{\"question\":\"Why is data integrity critical in smart grids?\",\"answer\":\"Data integrity is required for reliable grid operations and security, and it supports optimized generation, distribution, and consumption in smart city systems.\"},{\"question\":\"What challenges make traditional centralized models insufficient?\",\"answer\":\"They struggle with data privacy concerns, high communication overhead, and limited adaptiveness to dynamic conditions caused by renewable uncertainty and flexible loads like electric vehicles.\"},{\"question\":\"How does the proposed method enhance privacy while improving forecasting?\",\"answer\":\"Local machine-learning models are trained on distributed private datasets at grid stations, and only model parameters are communicated to a central server to build an aggregated global model without sharing raw data.\"}]","Adaptive Machine Learning Techniques for Enhancing Smart Grid Data Integrity - 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