Forecasting of G7 Countries' Total Energy Production: A Rigorous Exploration with Artificial Neural Networks and Multiple Linear Regression

Forecasting of G7 Countries' Total Energy Production: A Rigorous Exploration with Artificial Neural Networks and Multiple Linear Regression | 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 Forecasting of G7 Countries' Total Energy Production: A Rigorous Exploration with Artificial Neural Networks and Multiple Linear Regression Gökhan BAYIR, Faruk KILIÇ, Faik Ümit DİRİ, Hande ERKAYMAZ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4453981/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract The G7 countries, consisting of Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States, have important collaborations in energy production to achieve energy security. One of the main systems of Artificial intelligence's, artificial neural networks (ANN), is crucial to this area of study, comparatively using Multiple Linear Regression (MLR) comparatively. ANN and MLR are feasible to use across the G7 countries' total energy production numbers, and these numbers were determined using ANN and MLR forecasting techniques. The data included the years 1990–2020, focusing on GDP intensity, refined oil product production, electricity production, and renewable energy proportion. In ANN modeling, the best regression results at 10*10 have been obtained with two hidden layers. All regression values were 0.99947, with the training regression value being 0.99912, the validation regression value being 0.99997, and the test regression value being 0.99997. The results showed high accuracy, with regression scores exceeding 99% and smaller prediction error values. A paired sample t test has been applied to see whether the distinction between the average values is significant or not. The results of the test between the actual and predicted values (p = 0.7462 > 0.05) revealed that the forecasted values have been quite close to the actual values. Total energy production Mean Absolute Deviation (MAD), Mean Squared Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) parameters have been calculated as 4.364, 34.072, 5.837, and 0.162, respectively. The research proved that ANNs are effective in forecasting total energy output. And, with MLR, error values for MAD, MSE, RMSE, and MAPE were also found to be 5.364, 34.352, 5.861, and 1.609, respectively, using MLR modeling. By 2035, the G7 nations are expected to produce 50,652.746 Mtoe of energy collectively. The research proved that ANNs are effective in forecasting total energy output. G7 Countries Artificial Neural Networks Multiple Linear Regression Total Energy Production Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 10 Jul, 2024 Reviews received at journal 09 Jul, 2024 Reviewers agreed at journal 09 Jul, 2024 Reviewers agreed at journal 09 Jul, 2024 Reviews received at journal 09 Jul, 2024 Reviewers agreed at journal 08 Jul, 2024 Reviewers agreed at journal 08 Jul, 2024 Reviewers agreed at journal 08 Jul, 2024 Reviewers invited by journal 08 Jul, 2024 Editor assigned by journal 23 May, 2024 Submission checks completed at journal 22 May, 2024 First submitted to journal 21 May, 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. 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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-4453981","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":308892326,"identity":"f5f2b9e6-9034-47e7-bede-9e09edbbd063","order_by":0,"name":"Gökhan BAYIR","email":"","orcid":"","institution":"National Defence University, Military Academy Campus Ministries","correspondingAuthor":false,"prefix":"","firstName":"Gökhan","middleName":"","lastName":"BAYIR","suffix":""},{"id":308892327,"identity":"b15b8422-5359-4a1e-9aea-b5c977c8190d","order_by":1,"name":"Faruk KILIÇ","email":"","orcid":"","institution":"National Defence University, Military Academy Campus Ministries","correspondingAuthor":false,"prefix":"","firstName":"Faruk","middleName":"","lastName":"KILIÇ","suffix":""},{"id":308892328,"identity":"74c7ada5-4691-4489-abb7-98144ef255c6","order_by":2,"name":"Faik Ümit DİRİ","email":"","orcid":"","institution":"National Defence University, Military Academy Campus Ministries","correspondingAuthor":false,"prefix":"","firstName":"Faik","middleName":"Ümit","lastName":"DİRİ","suffix":""},{"id":308892329,"identity":"d3cd104e-4810-4291-8e7a-8f99b26abc96","order_by":3,"name":"Hande ERKAYMAZ","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYBACAwYGZgiLvcEMLipBnBaeAyhaDIjQIpFApBZz9t7HBj/btsnLz3y87cEHhm32BgeYD97mYfiTj0uLZc9x48TettuGjbPTyg1nMNxO3HCALdmah8HAsgGXw26kMR/gbbvN2CydYybNw3A7weAAD4hhgNNlBvefMR/823bbvk3yDFgL0GH83/BrucHGnAy0JbFHAmz4bcYNB3jY8Gs5k8ZsLHPudvIMnrQyyRkGtxNnHmYztpxjYIxby/FjzJJvym7bzm8/vE3iQ8Vte77jzQ9vvKmQwx3KIMDIBjeBARpP+DUAwR9CCkbBKBgFo2BEAwBg7lA2ju2QQwAAAABJRU5ErkJggg==","orcid":"","institution":"National Defence University, Naval Academy Istanbul","correspondingAuthor":true,"prefix":"","firstName":"Hande","middleName":"","lastName":"ERKAYMAZ","suffix":""}],"badges":[],"createdAt":"2024-05-21 10:06:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4453981/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4453981/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57584613,"identity":"e46669db-44d5-41b7-930b-a047cf0c65dc","added_by":"auto","created_at":"2024-06-03 02:52:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":983562,"visible":true,"origin":"","legend":"","description":"","filename":"Forecastingson.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4453981/v1_covered_3f4c2e8f-9246-4e25-97ba-85c947e75c6c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Forecasting of G7 Countries' Total Energy Production: A Rigorous Exploration with Artificial Neural Networks and Multiple Linear Regression","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"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":"G7 Countries, Artificial Neural Networks, Multiple Linear Regression, Total Energy Production","lastPublishedDoi":"10.21203/rs.3.rs-4453981/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4453981/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe G7 countries, consisting of Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States, have important collaborations in energy production to achieve energy security. One of the main systems of Artificial intelligence's, artificial neural networks (ANN), is crucial to this area of study, comparatively using Multiple Linear Regression (MLR) comparatively. ANN and MLR are feasible to use across the G7 countries' total energy production numbers, and these numbers were determined using ANN and MLR forecasting techniques. The data included the years 1990\u0026ndash;2020, focusing on GDP intensity, refined oil product production, electricity production, and renewable energy proportion. In ANN modeling, the best regression results at 10*10 have been obtained with two hidden layers. All regression values were 0.99947, with the training regression value being 0.99912, the validation regression value being 0.99997, and the test regression value being 0.99997. The results showed high accuracy, with regression scores exceeding 99% and smaller prediction error values. A paired sample t test has been applied to see whether the distinction between the average values is significant or not. The results of the test between the actual and predicted values (p\u0026thinsp;=\u0026thinsp;0.7462\u0026thinsp;\u0026gt;\u0026thinsp;0.05) revealed that the forecasted values have been quite close to the actual values. Total energy production Mean Absolute Deviation (MAD), Mean Squared Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) parameters have been calculated as 4.364, 34.072, 5.837, and 0.162, respectively. The research proved that ANNs are effective in forecasting total energy output. And, with MLR, error values for MAD, MSE, RMSE, and MAPE were also found to be 5.364, 34.352, 5.861, and 1.609, respectively, using MLR modeling. By 2035, the G7 nations are expected to produce 50,652.746 Mtoe of energy collectively. 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