Optimizing Energy Efficiency and Comfort in Rural Self-Built Housing: A Comparative Study of GA and EA Algorithms

Optimizing Energy Efficiency and Comfort in Rural Self-Built Housing: A Comparative Study of GA and EA Algorithms | 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 Optimizing Energy Efficiency and Comfort in Rural Self-Built Housing: A Comparative Study of GA and EA Algorithms Chen Chen, Huifang Wang, Dayao Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5885534/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 With the growing global focus on sustainable building design, reducing energy consumption and carbon emissions in buildings has become a critical research topic. This study explores the variations in electricity consumption, carbon emissions, and comfort levels influenced by building design parameters, and compares the applications of Genetic Algorithm (GA) and Evolutionary Algorithm (EA) in building optimization. The research is based on a Private residence in Huzhou, Zhejiang Province, where actual data were collected, including wall-to-window ratio, heating set point, cooling set point, roof shading coefficient, equipment power density, airtightness, and thermal transmittance of external walls, to construct a detailed building model. During the optimization process, DesignBuilder software was utilized for modeling, and both GA and EA optimization algorithms were implemented. The simulation results show that GA outperforms EA in terms of convergence speed, stability, and balancing multiple objectives. Specifically, GA reduced the annual electricity consumption from 862.26 kWh to 825.32 kWh, a 4.28% decrease, with the standard deviation dropping from 287.61 to 275.43 (a 4.25% improvement in stability). Discomfort time was significantly reduced by 31.56%, from 1030.2 hours to 705.12 hours, with substantial reductions in both standard deviation and interquartile range (IQR). Carbon emissions decreased from 552.62 kg to 533.86 kg, a 3.39% reduction, highlighting the potential for emission reduction. The results indicate significant differences between the two algorithms in optimizing building energy consumption, reducing carbon emissions, and enhancing comfort levels. GA demonstrates advantages in convergence speed and result accuracy, while EA excels in solution diversity and global search capability. Ultimately, this paper presents optimization recommendations based on both algorithms, aimed at providing a reference for future sustainable building design. Physical sciences/Engineering/Civil engineering Earth and environmental sciences/Environmental social sciences/Sustainability building energy consumption carbon emissions comfort genetic algorithm evolutionary algorithm building optimization 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-5885534","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":406895559,"identity":"d7302852-4a0f-46b7-abb2-51ee18e58134","order_by":0,"name":"Chen Chen","email":"","orcid":"","institution":"The University of Kitakyushu","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Chen","suffix":""},{"id":406895560,"identity":"645705cf-2a36-4f72-8ecf-c71e882470bd","order_by":1,"name":"Huifang Wang","email":"","orcid":"","institution":"The University of Kitakyushu","correspondingAuthor":false,"prefix":"","firstName":"Huifang","middleName":"","lastName":"Wang","suffix":""},{"id":406895561,"identity":"621fdf71-4019-460a-822e-23b4deeb53da","order_by":2,"name":"Dayao Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYBACPoYExseMDWC2AXFa2BgSmI1J1sImTaIW9hyz6sIdtYkN7M3bJBhq7hChheeN2e2ZZ44nNvAcK5NgOPaMCC0SOWa3eduOJTYAGRKMDYeJ01IM1iL/hgQtzLxtNUBbeIjVwvOsWHrmmQPGbTxpxRYJx4jQws+evPFz4Y462X72wxtvfKghQgsDAwcoOg4DIwgIEojRwMDA/gBI1BGndhSMglEwCkYmAAAgjjaSIS1NYAAAAABJRU5ErkJggg==","orcid":"","institution":"Beibu Gulf University","correspondingAuthor":true,"prefix":"","firstName":"Dayao","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-01-23 06:23:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5885534/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5885534/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89873273,"identity":"aa489b46-f911-4ccc-8647-bc4ea076a28e","added_by":"auto","created_at":"2025-08-26 03:16:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":828981,"visible":true,"origin":"","legend":"","description":"","filename":"scientificreports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5885534/v1_covered_bf51e2f3-3c72-4dea-9699-0c6890677960.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimizing Energy Efficiency and Comfort in Rural Self-Built Housing: A Comparative Study of GA and EA Algorithms","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"building energy consumption, carbon emissions, comfort, genetic algorithm, evolutionary algorithm, building optimization","lastPublishedDoi":"10.21203/rs.3.rs-5885534/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5885534/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the growing global focus on sustainable building design, reducing energy consumption and carbon emissions in buildings has become a critical research topic. This study explores the variations in electricity consumption, carbon emissions, and comfort levels influenced by building design parameters, and compares the applications of Genetic Algorithm (GA) and Evolutionary Algorithm (EA) in building optimization. The research is based on a Private residence in Huzhou, Zhejiang Province, where actual data were collected, including wall-to-window ratio, heating set point, cooling set point, roof shading coefficient, equipment power density, airtightness, and thermal transmittance of external walls, to construct a detailed building model.\u003c/p\u003e \u003cp\u003eDuring the optimization process, DesignBuilder software was utilized for modeling, and both GA and EA optimization algorithms were implemented. The simulation results show that GA outperforms EA in terms of convergence speed, stability, and balancing multiple objectives. Specifically, GA reduced the annual electricity consumption from 862.26 kWh to 825.32 kWh, a 4.28% decrease, with the standard deviation dropping from 287.61 to 275.43 (a 4.25% improvement in stability). Discomfort time was significantly reduced by 31.56%, from 1030.2 hours to 705.12 hours, with substantial reductions in both standard deviation and interquartile range (IQR). Carbon emissions decreased from 552.62 kg to 533.86 kg, a 3.39% reduction, highlighting the potential for emission reduction. The results indicate significant differences between the two algorithms in optimizing building energy consumption, reducing carbon emissions, and enhancing comfort levels. GA demonstrates advantages in convergence speed and result accuracy, while EA excels in solution diversity and global search capability. 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