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        <title type="main" level="a">Retrofitting of Buildings to Improve Energy Efficiency: A Comprehensive Systematic Literature Review and Future Research Directions</title>
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          <persName n="1" ref="https://orcid.org/0000-0002-7248-8864" type="ORCID">
            <forename>Elena</forename>
            <surname>Imani</surname>
            <placeName type="affiliation">Teesside university, United Kingdom</placeName>
          </persName>
          <persName n="2" ref="https://orcid.org/0000-0003-1032-7560" type="ORCID">
            <forename>Huda</forename>
            <surname>Dawood</surname>
            <placeName type="affiliation">Teesside university, United Kingdom</placeName>
          </persName>
          <persName n="3" ref="https://orcid.org/0000-0002-4873-7576" type="ORCID">
            <forename>Nashwan</forename>
            <surname>Dawood</surname>
            <placeName type="affiliation">Teesside University, United Kingdom</placeName>
          </persName>
          <persName n="4" ref="https://orcid.org/0000-0001-6075-1496" type="ORCID">
            <forename>Annalisa</forename>
            <surname>Occhipinti</surname>
            <placeName type="affiliation">Teesside university, United Kingdom</placeName>
          </persName>
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          <resp>This is a section of <title>CONVR 2023 - Proceedings of the 23rd International Conference on  Construction Applications of Virtual Reality </title>(DOI: <idno type="DOI">10.36253/979-12-215-0289-3</idno>) by </resp>
          <name>Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi</name>
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        <publisher>Firenze University Press</publisher>
        <pubPlace>Florence</pubPlace>
        <date when="2023">2023</date>
        <idno type="DOI">https://doi.org/10.36253/10.36253/979-12-215-0289-3.109</idno>
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          <p>Available for academic research purposes</p>
          <p>Open Access</p>
          <p>Copyright Author(s)</p>
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            <p>Content licence CC BY-NC 4.0</p>
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        <p>This is original content, published for academic research purposes</p>
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      <abstract xml:lang="en">
        <p>A large body of research has been developed with the aim of assisting policymakers in setting ambitious and achievable environmental targets for the retrofit of current and future building types for energy-efficiency and in creating effective retrofit strategies to meet these targets. The aim of this research is to conduct a comprehensive study to identify the relationship between building type and sustainability, with a particular emphasis on retrofitting and try to identify research gaps in the most effective energy-saving strategies for retrofitting various types of buildings. In this regard, this study conducts a systematic literature review (SLR) utilizes artificial intelligence (AI) and natural language processing (NLP). Sixty relevant papers are selected and reviewed, establishing a comprehensive searching scheme. The research highlights retrofitting strategies for improving energy efficiency in buildings and discuss the limitations of current practises in terms of physical and technical developments, such as utilising new energy systems and innovative retrofitting materials. To overcome these, future studies could focus on in-depth building classification, developing tailored retrofitting alternatives, and establishing an adaptive solution framework. This framework aligns cohesively with diverse typologies, adapting to changing contexts and enhancing long-term performance</p>
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            <item>retrofitting</item>
            <item>typology of building</item>
            <item>building energy performance</item>
            <item>residential buildings</item>
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      <p>It is available online at https://doi.org/10.36253/10.36253/979-12-215-0289-3.109<ref target="https://doi.org/10.36253/10.36253/979-12-215-0289-3.109" /></p>
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