Electricity Consumption Prediction Based On Autoregressive Kalman Filtering

Electricity Consumption Prediction Based On Autoregressive Kalman Filtering | 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 Electricity Consumption Prediction Based On Autoregressive Kalman Filtering Zuyuan Yang, Zitan Xie, Zhiwei Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4878573/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Nov, 2024 Read the published version in Electrical Engineering → Version 1 posted 9 You are reading this latest preprint version Abstract Electricity consumption prediction is crucial for energy suppliers and industrial companies as it aids in optimizing energy planning and reducing energy consumption losses. Existing methods primarily focus on the time series relationships of individual nodes or components, overlooking the spatial structure of node groups, which leads to insufficient prediction accuracy. To overcome this limitation, we propose an autoregressive Kalman filtering (AKF) method for electricity consumption prediction. Our primary contribution lies in the innovative design of the Kalman filter observation equation in AKF, which finely adjusts the initial predictions of the autoregressive (AR) model based on the hierarchical structure of equipment. This approach comprehensively considers the interrelationships among equipment levels, significantly enhancing prediction accuracy. Specifically, we first utilize the autoregressive model to capture the autocorrelation of the sequence, forming the basis for constructing the state equation in the Kalman filter. In designing the observation equation, we simplify the model and reduce the complexity of parameter estimation by setting the sum of predicted electricity consumption values of sub-node components as the observed value for the total node components. To validate the effectiveness of our proposed method, experiments were conducted using real electricity consumption data from Foshan Ceramic Factory. The results demonstrate significant improvements in prediction accuracy compared to baseline methods such as BP, LSTM, GA-BP, PSO-SVM, and AR. electricity consumption Kalman filtering autoregressive model hierarchical structure Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 12 Nov, 2024 Read the published version in Electrical Engineering → Version 1 posted Editorial decision: Revision requested 08 Sep, 2024 Reviews received at journal 07 Sep, 2024 Reviewers agreed at journal 29 Aug, 2024 Reviews received at journal 22 Aug, 2024 Reviewers agreed at journal 22 Aug, 2024 Reviewers invited by journal 22 Aug, 2024 Editor assigned by journal 09 Aug, 2024 Submission checks completed at journal 08 Aug, 2024 First submitted to journal 08 Aug, 2024 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-4878573","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":348551391,"identity":"ec4db33d-7aa9-4aa2-b3d9-dfdd54015b5d","order_by":0,"name":"Zuyuan Yang","email":"","orcid":"","institution":"School of Automation, Guangdong Key Laboratory of IoT Information Technology, Guangdong University of Technology, Guangzhou, 510006","correspondingAuthor":false,"prefix":"","firstName":"Zuyuan","middleName":"","lastName":"Yang","suffix":""},{"id":348551392,"identity":"2c90cfb1-c72b-4b55-99d5-44d3cde08749","order_by":1,"name":"Zitan Xie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYDACZhjJ3sB4IIE4LcwQLTw8BxiI1MIA0yKRwHCAKA3y7fwHPxf8sk7cL/n4wYGHbYfzGNgPH92ATwtjMzOz9My+9MQe6TSDA4lth4sZeNLSbuB1FtAv0rw9h4FaEsBaEhskeMzwamED6vkN1iJ5/ANxWniYmdmkeX4AtUjwEGmLBDOzmTVvQ7pxz5mcggMJ59IT2wj5Rb7/4OPbPH+sZdvbj298+KPMOrGf/fAxvFrAgLENxmAD+o6gcjD4g8EYBaNgFIyCUYAAAM/BSz0lE3akAAAAAElFTkSuQmCC","orcid":"","institution":"School of Automation, Guangdong Key Laboratory of IoT Information Technology, Guangdong University of Technology, Guangzhou, 510006","correspondingAuthor":true,"prefix":"","firstName":"Zitan","middleName":"","lastName":"Xie","suffix":""},{"id":348551393,"identity":"5ee57350-88ac-4361-8437-965c097cf9f3","order_by":2,"name":"Zhiwei Huang","email":"","orcid":"","institution":"South China University of Technology, Guangzhou, 510641, Guangdong","correspondingAuthor":false,"prefix":"","firstName":"Zhiwei","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2024-08-08 06:30:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4878573/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4878573/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00202-024-02833-7","type":"published","date":"2024-11-12T15:57:30+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":69274992,"identity":"211a17a4-d647-4e9d-8f19-e213d3925a5d","added_by":"auto","created_at":"2024-11-18 16:43:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":944902,"visible":true,"origin":"","legend":"","description":"","filename":"AKF.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4878573/v1_covered_9082250c-77c9-4e73-af5f-13ea3443e770.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Electricity Consumption Prediction Based On Autoregressive Kalman Filtering","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"electrical-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"elen","sideBox":"Learn more about [Electrical Engineering](http://link.springer.com/journal/202)","snPcode":"202","submissionUrl":"https://submission.nature.com/new-submission/202/3","title":"Electrical Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"electricity consumption, Kalman filtering, autoregressive model, hierarchical structure","lastPublishedDoi":"10.21203/rs.3.rs-4878573/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4878573/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eElectricity consumption prediction is crucial for energy suppliers and industrial companies as it aids in optimizing energy planning and reducing energy consumption losses. Existing methods primarily focus on the time series relationships of individual nodes or components, overlooking the spatial structure of node groups, which leads to insufficient prediction accuracy. To overcome this limitation, we propose an autoregressive Kalman filtering (AKF) method for electricity consumption prediction. Our primary contribution lies in the innovative design of the Kalman filter observation equation in AKF, which finely adjusts the initial predictions of the autoregressive (AR) model based on the hierarchical structure of equipment. This approach comprehensively considers the interrelationships among equipment levels, significantly enhancing prediction accuracy. Specifically, we first utilize the autoregressive model to capture the autocorrelation of the sequence, forming the basis for constructing the state equation in the Kalman filter. In designing the observation equation, we simplify the model and reduce the complexity of parameter estimation by setting the sum of predicted electricity consumption values of sub-node components as the observed value for the total node components. To validate the effectiveness of our proposed method, experiments were conducted using real electricity consumption data from Foshan Ceramic Factory. The results demonstrate significant improvements in prediction accuracy compared to baseline methods such as BP, LSTM, GA-BP, PSO-SVM, and AR.\u003c/p\u003e","manuscriptTitle":"Electricity Consumption Prediction Based On Autoregressive Kalman Filtering","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-04 18:15:52","doi":"10.21203/rs.3.rs-4878573/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-08T08:31:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-08T03:39:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"275602779609741285130801660164118615226","date":"2024-08-30T03:12:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-22T14:26:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"170954406543369689677295189883311751368","date":"2024-08-22T12:43:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-22T12:00:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-09T13:55:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-08T12:04:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Electrical Engineering","date":"2024-08-08T06:29:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"electrical-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"elen","sideBox":"Learn more about [Electrical Engineering](http://link.springer.com/journal/202)","snPcode":"202","submissionUrl":"https://submission.nature.com/new-submission/202/3","title":"Electrical Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4b243505-105e-467d-902e-a123f5c151bb","owner":[],"postedDate":"September 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-18T16:02:05+00:00","versionOfRecord":{"articleIdentity":"rs-4878573","link":"https://doi.org/10.1007/s00202-024-02833-7","journal":{"identity":"electrical-engineering","isVorOnly":false,"title":"Electrical Engineering"},"publishedOn":"2024-11-12 15:57:30","publishedOnDateReadable":"November 12th, 2024"},"versionCreatedAt":"2024-09-04 18:15:52","video":"","vorDoi":"10.1007/s00202-024-02833-7","vorDoiUrl":"https://doi.org/10.1007/s00202-024-02833-7","workflowStages":[]},"version":"v1","identity":"rs-4878573","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4878573","identity":"rs-4878573","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}