Research on optimizing the networking mode of low-voltage distribution network in Internet of Things system based on improved ACO

Research on optimizing the networking mode of low-voltage distribution network in Internet of Things system based on improved ACO | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Research on optimizing the networking mode of low-voltage distribution network in Internet of Things system based on improved ACO Long Xu, Xiao Liu, Jie Han, Quanli Lin, Yongxi Huang, Huisheng Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6629433/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Optimizing the stability and transmission efficiency of low-voltage distribution networks remains a critical challenge in smart grid systems. This study proposes an AI-enhanced optimization framework that integrates an improved Ant Colony Optimization (ACO) algorithm with intelligent Internet of Things (IoT) sensing technologies. The ACO algorithm, inspired by swarm intelligence principles, dynamically adjusts network paths to minimize power loss, while IoT devices provide real-time data on grid load and voltage distribution, enabling adaptive decision-making. Experimental results demonstrate that the AI-driven model reduces power loss by 61.8835 kW (30.53%) and 91.9952 kW (45.39%) in pre- and post-reconstruction scenarios, respectively. Additionally, the minimum node voltage improves from 0.9133 p.u. to 0.9291 p.u. (pre-reconstruction) and 0.9453 p.u. (post-reconstruction), with system-wide voltage stability enhancements. Theoretical and simulation analyses confirm that the synergy of AI-based optimization and IoT intelligence significantly improves transmission efficiency and operational robustness in low-voltage grids. AI-driven ant colony optimization IoT-enabled smart grid Low-voltage network optimization Swarm intelligence Adaptive energy systems Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6629433","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":466298199,"identity":"27089053-c78c-4c47-93d7-c4735015939d","order_by":0,"name":"Long 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