The distributed co-evolution model of cloud-edge-device distribution network structure combined with artificial intelligence under the new energy situation

The distributed co-evolution model of cloud-edge-device distribution network structure combined with artificial intelligence under the new energy situation | 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 Article The distributed co-evolution model of cloud-edge-device distribution network structure combined with artificial intelligence under the new energy situation Fei Zhou, Chunpeng Wu, Yue Wang, Qinghe Ye, Zhenying Tai, Haoyi Zhou, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6942261/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 The complex electricity consumption situation on the user demand side and the large-scale power generation of renewable energy have gradually shifted the mode of source following load in the power system to the mode of source and load interaction. At present, the voltage regulation methods all require a large amount of computing resources to accurately predict the fluctuating load in the face of the new energy structure. However, with the development of artificial intelligence and cloud computing, the processing of huge databases and the release of computing resources have become possible. This paper proposes a new method for user-end power analysis based on the combination of traditional mathematical statistics and machine learning methods to make up for the deficiencies of non-intrusive load detection methods and construct a distributed optimization of cloud-edge-device distribution networks based on user requirements. Aiming at problems such as overfitting and the demand for accurate short-term renewable power generation power prediction, it is proposed to use the long short-term memory method to extract data information, and combine the deep neural network to construct a coupling algorithm to obtain the output prediction of renewable energy under the collaboration of cloud-edge-device. The R2 value of the coupling algorithm reaches 0.991, while the values of RMSE, MAPE and MAE are 1347.2, 5.36 and 199.4 respectively. Predicted power prediction cannot completely eliminate errors. It is necessary to combine the consistency algorithm to construct the regulation strategy. Under the control of the regulation strategy, stability can be achieved after 25 iterations. The cost increase rate is 0.241 yuan /kWh, and the optimal regulation powers of each cluster are 6.42, 8.3, 3.21, 0.67, 0.43 and 0.58 kW respectively. Finally, the cloud-edge-device distributed coevolution model of the power grid is obtained to achieve the economy and security of power grid voltage control. Physical sciences/Energy science and technology Physical sciences/Engineering Cloud-edge-end distribution network New situation of energy Artificial intelligence Distributed collaboration 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. 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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-6942261","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":475446111,"identity":"64134a76-7b57-4e06-bc94-3900bd69dfbf","order_by":0,"name":"Fei 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At present, the voltage regulation methods all require a large amount of computing resources to accurately predict the fluctuating load in the face of the new energy structure. However, with the development of artificial intelligence and cloud computing, the processing of huge databases and the release of computing resources have become possible. This paper proposes a new method for user-end power analysis based on the combination of traditional mathematical statistics and machine learning methods to make up for the deficiencies of non-intrusive load detection methods and construct a distributed optimization of cloud-edge-device distribution networks based on user requirements. Aiming at problems such as overfitting and the demand for accurate short-term renewable power generation power prediction, it is proposed to use the long short-term memory method to extract data information, and combine the deep neural network to construct a coupling algorithm to obtain the output prediction of renewable energy under the collaboration of cloud-edge-device. The R2 value of the coupling algorithm reaches 0.991, while the values of RMSE, MAPE and MAE are 1347.2, 5.36 and 199.4 respectively. Predicted power prediction cannot completely eliminate errors. It is necessary to combine the consistency algorithm to construct the regulation strategy. Under the control of the regulation strategy, stability can be achieved after 25 iterations. The cost increase rate is 0.241 yuan /kWh, and the optimal regulation powers of each cluster are 6.42, 8.3, 3.21, 0.67, 0.43 and 0.58 kW respectively. 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