<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<TEI xmlns="http://www.tei-c.org/ns/1.0">
  <teiHeader>
    <fileDesc>
      <titleStmt>
        <title type="main" level="a">Solar Potential and Energy Assessment Data in U-BEM Models: Interoperability Analysis Between Performance Simulation Tools and OpenBIM/GIS Platforms</title>
        <author>
          <persName n="1" ref="https://orcid.org/0000-0002-8185-4301" type="ORCID">
            <forename>CARLO</forename>
            <surname>ZANCHETTA</surname>
            <placeName type="affiliation">University of Padua, Italy</placeName>
          </persName>
          <persName n="2" ref="https://orcid.org/0009-0005-7521-901X" type="ORCID">
            <forename>Martina</forename>
            <surname>Giorio</surname>
            <placeName type="affiliation">University of Padua, Italy</placeName>
          </persName>
          <persName n="3">
            <forename>Maria Grazia</forename>
            <surname>Donatiello</surname>
            <placeName type="affiliation">University of Padua, Italy</placeName>
          </persName>
          <persName n="4">
            <forename>Federico</forename>
            <surname>Rossi</surname>
            <placeName type="affiliation">University of Padua, Italy</placeName>
          </persName>
          <persName n="5" ref="https://orcid.org/0000-0002-5989-995X" type="ORCID">
            <forename>Rossana</forename>
            <surname>Paparella</surname>
            <placeName type="affiliation">University of Padua, Italy</placeName>
          </persName>
        </author>
        <respStmt>
          <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>
        </respStmt>
      </titleStmt>
      <publicationStmt>
        <publisher>Firenze University Press</publisher>
        <pubPlace>Florence</pubPlace>
        <date when="2023">2023</date>
        <idno type="DOI">https://doi.org/10.36253/10.36253/979-12-215-0289-3.102</idno>
        <availability>
          <p>Available for academic research purposes</p>
          <p>Open Access</p>
          <p>Copyright Author(s)</p>
          <licence source="text" target="https://creativecommons.org/licenses/by-nc/4.0/legalcode">
            <p>Content licence CC BY-NC 4.0</p>
          </licence>
          <licence source="metadata" target="https://creativecommons.org/publicdomain/zero/1.0/legalcode">
            <p>Metadata licence CC0 1.0</p>
          </licence>
        </availability>
      </publicationStmt>
      <sourceDesc>
        <p>This is original content, published for academic research purposes</p>
      </sourceDesc>
    </fileDesc>
    <encodingDesc>
      <appInfo>
        <application version="2.2" ident="Booksflow">
          <desc>Digital edition XML powered by Booksflow</desc>
        </application>
      </appInfo>
    </encodingDesc>
    <profileDesc>
      <abstract xml:lang="en">
        <p>To evaluate the energy and solar potential of the building stock and address feasibility studies of building retrofit interventions information standards are required to ensure proper data flow from building and urban models to simulation environments. Energy performance data are gathered from different information containers and therefore the result of simulations needs to be shared in BIM/GIS environments to better address energy policies and decision-making processes. Solar potential and energy retrofit estimation, developed by means of urban models (U-BEM) are too rough to support a decision-making process, even if at a feasibility stage. On the opposite, strategic decisions are defined with reference to large building stocks that require a U-BEM approach. To increase the reliability of this kind of simulations the study proposes to integrate U-BEMS with BIM-based data that are aggregated and published at urban scale as average performance indicators of built systems. The interoperability problem is analyzed both for simulation tools that need to manage this kind of data and openBIM/GIS platforms that need to share performance indicators and simulation results</p>
      </abstract>
      <textClass>
        <keywords>
          <list>
            <item>Energy potential</item>
            <item>Solar potential</item>
            <item>IFC</item>
            <item>BIM</item>
            <item>U-BEM</item>
          </list>
        </keywords>
      </textClass>
    </profileDesc>
  </teiHeader>
  <text>
    <body>
      <p>It is available online at https://doi.org/10.36253/10.36253/979-12-215-0289-3.102<ref target="https://doi.org/10.36253/10.36253/979-12-215-0289-3.102" /></p>
      <div>
        <listBibl>
          <head>References</head>
          <bibl n="139449">
            <bibl>Amado, M., &amp;amp; Poggi, F. (2012). Towards solar urban planning: A new step for better energy performance. Energy Procedia, 30.</bibl>
            <idno type="DOI">10.1016/j.egypro.2012.11.139</idno>
          </bibl>
          <bibl n="138725">
            <bibl>Assouline, D., Mohajeri, N., &amp;amp; Scartezzini, J. L. (2017). Quantifying rooftop photovoltaic solar energy potential: A machine learning approach. Solar Energy, 141, 278–296.</bibl>
            <idno type="DOI">10.1016/j.solener.2016.11.045</idno>
          </bibl>
          <bibl n="137760">
            <bibl>Bahu, J. M., Koch, A., Kremers, E., &amp;amp; Murshed, S. M. (2013). Towards a 3D spatial urban energy modelling approach. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2(2W1), 33–41.</bibl>
            <idno type="DOI">10.5194/isprsannals-II-2-W1-33-2013</idno>
          </bibl>
          <bibl n="139260">Behr, F.-J. 1957-, &amp;amp; AGSE. 5 2012 Stuttgart. (2012). Geoinformation - catalyst for planning, development and good governance. AGSE Publishing.</bibl>
          <bibl n="138810">Boriani, M., Giambruno, M., &amp;amp; Garzulino, A. (2011). Studio, sviluppo e definizione di schede tecniche di intervento per l’efficienza energetica negli edifici di pregio.</bibl>
          <bibl n="138401">
            <bibl>Borkowska, S., &amp;amp; Pokonieczny, K. (2022). Analysis of OpenStreetMap Data Quality for Selected Counties in Poland in Terms of Sustainable Development. Sustainability (Switzerland), 14(7).</bibl>
            <idno type="DOI">10.3390/su14073728</idno>
          </bibl>
          <bibl n="138353">
            <bibl>Bshouty, E., Shafir, A., &amp;amp; Dalyot, S. (2020). Towards the generation of 3D OpenStreetMap building models from single contributed photographs. Computers, Environment and Urban Systems, 79.</bibl>
            <idno type="DOI">10.1016/j.compenvurbsys.2019.101421</idno>
          </bibl>
          <bibl n="139206">Corrado, V., Ballarini, I., &amp;amp; Corgnati, S. P. (2014). Building Typology Brochure-Italy Fascicolo sulla Tipologia Edilizia Italiana nuova edizione.</bibl>
          <bibl n="137612">Diana, L. (2017). Metodo CRI_TRA: un metodo di valutazione comparativa&amp;#160; delle criticit&amp;#224; e della trasformabilit&amp;#224; edilizia del&amp;#160; patrimonio residenziale pubblico in Italia. https://www.researchgate.net/publication/317002914</bibl>
          <bibl n="137833">
            <bibl>Dorn, H., T&amp;#246;rnros, T., &amp;amp; Zipf, A. (2015). Quality evaluation of VGI using authoritative data-a comparison with land use data in southern Germany. ISPRS International Journal of Geo-Information, 4(3), 1657–1671.</bibl>
            <idno type="DOI">10.3390/ijgi4031657</idno>
          </bibl>
          <bibl n="137459">
            <bibl>Elwood, S., Goodchild, M. F., &amp;amp; Sui, D. Z. (2012). Researching Volunteered Geographic Information: Spatial Data, Geographic Research, and New Social Practice. Annals of the Association of American Geographers, 102(3), 571–590.</bibl>
            <idno type="DOI">10.1080/00045608.2011.595657</idno>
          </bibl>
          <bibl n="139672">EPW Map. (n.d.). Retrieved August 7, 2023, from https://www.ladybug.tools/epwmap/</bibl>
          <bibl n="139020">
            <bibl>Esclap&amp;#233;s, J., Ferreiro, I., Piera, J., &amp;amp; Teller, J. (2014). A method to evaluate the adaptability of photovoltaic energy on urban fa&amp;#231;ades. Solar Energy, 105.</bibl>
            <idno type="DOI">10.1016/j.solener.2014.03.012</idno>
          </bibl>
          <bibl n="138251">
            <bibl>Freitas, S., Catita, C., Redweik, P., &amp;amp; Brito, M. C. (2015). Modelling solar potential in the urban environment: State-of-the-art review. In Renewable and Sustainable Energy Reviews (Vol. 41).</bibl>
            <idno type="DOI">10.1016/j.rser.2014.08.060</idno>
          </bibl>
          <bibl n="137162">
            <bibl>Giannelli, D., Le&amp;#243;n-S&amp;#225;nchez, C., &amp;amp; Agugiaro, G. (2022). Comparison and evaluation of different gis software tools to estimate solar irradiation. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 5(4), 275–282.</bibl>
            <idno type="DOI">10.5194/isprs-Annals-V-4-2022-275-2022</idno>
          </bibl>
          <bibl n="139548">
            <bibl>Goodchild, M. F. (2007). Citizens as sensors: The world of volunteered geography. GeoJournal, 69(4), 211–221.</bibl>
            <idno type="DOI">10.1007/s10708-007-9111-y</idno>
          </bibl>
          <bibl n="139630">Haklay, M., &amp;amp; Weber, P. (2008). openstreetMap: User-Generated street Maps. www.openstreetmap.</bibl>
          <bibl n="139492">
            <bibl>Heipke, C. (2010). Crowdsourcing geospatial data. ISPRS Journal of Photogrammetry and Remote Sensing, 65(6), 550–557.</bibl>
            <idno type="DOI">10.1016/j.isprsjprs.2010.06.005</idno>
          </bibl>
          <bibl n="139504">Italy/PCN - OpenStreetMap Wiki. (n.d.). Retrieved August 7, 2023, from https://wiki.openstreetmap.org/wiki/Italy/PCN</bibl>
          <bibl n="137890">
            <bibl>Jakica, N. (2018). State-of-the-art review of solar design tools and methods for assessing daylighting and solar potential for building-integrated photovoltaics. Renewable and Sustainable Energy Reviews, 81.</bibl>
            <idno type="DOI">10.1016/j.rser.2017.05.080</idno>
          </bibl>
          <bibl n="138665">
            <bibl>Kabir, E., Kumar, P., Kumar, S., Adelodun, A. A., &amp;amp; Kim, K. H. (2018). Solar energy: Potential and future prospects. Renewable and Sustainable Energy Reviews, 82(1), 894–900.</bibl>
            <idno type="DOI">10.1016/j.rser.2017.09.094</idno>
          </bibl>
          <bibl n="137854">
            <bibl>Lan, H., Gou, Z., &amp;amp; Hou, C. (2022). Understanding the relationship between urban morphology and solar potential in mixed-use neighborhoods using machine learning algorithms. Sustainable Cities and Society, 87.</bibl>
            <idno type="DOI">10.1016/j.scs.2022.104225</idno>
          </bibl>
          <bibl n="139521">
            <bibl>Lee, J. G., &amp;amp; Kang, M. (2015). Geospatial Big Data: Challenges and Opportunities. Big Data Research, 2(2), 74–81.</bibl>
            <idno type="DOI">10.1016/j.bdr.2015.01.003</idno>
          </bibl>
          <bibl n="138252">
            <bibl>Lobaccaro, G., Lisowska, M. M., Saretta, E., Bonomo, P., &amp;amp; Frontini, F. (2019). A methodological analysis approach to assess solar energy potential at the neighborhood scale. Energies, 12(18).</bibl>
            <idno type="DOI">10.3390/en12183554</idno>
          </bibl>
          <bibl n="136957">
            <bibl>Manni, M., Nocente, A., Kong, G., Skeie, K., Fan, H., &amp;amp; Lobaccaro, G. (2022). Solar energy digitalization at high latitudes: A model chain combining solar irradiation models, a LiDAR scanner, and high-detail 3D building model. Frontiers in Energy Research, 10.</bibl>
            <idno type="DOI">10.3389/fenrg.2022.1082092</idno>
          </bibl>
          <bibl n="139366">Mazzarella, L., &amp;amp; Piter&amp;#224;, L. A. (n.d.). Efficienza Energetica attraverso la Diagnosi e il Servizio Energia negli Edifici Linee Guida.</bibl>
          <bibl n="139088">
            <bibl>Minaei, M. (2020). Evolution, density and completeness of OpenStreetMap road networks in developing countries: The case of Iran. Applied Geography, 119.</bibl>
            <idno type="DOI">10.1016/j.apgeog.2020.102246</idno>
          </bibl>
          <bibl n="138629">
            <bibl>Neis, P., &amp;amp; Zielstra, D. (2014). Recent Developments and Future Trends in Volunteered Geographic Information Research: The Case of OpenStreetMap. Future Internet, 6(1), 76–106.</bibl>
            <idno type="DOI">10.3390/fi6010076</idno>
          </bibl>
          <bibl n="139450">OpenStreetMap Statistics. (n.d.). Retrieved August 7, 2023, from https://planet.openstreetmap.org/statistics/data_stats.html</bibl>
          <bibl n="137711">
            <bibl>Peronato, G., Rastogi, P., Rey, E., &amp;amp; Andersen, M. (2018). A toolkit for multi-scale mapping of the solar energy-generation potential of buildings in urban environments under uncertainty. Solar Energy, 173, 861–874.</bibl>
            <idno type="DOI">10.1016/j.solener.2018.08.017</idno>
          </bibl>
          <bibl n="138471">
            <bibl>Rana, S., &amp;amp; Joliveau, T. (2009). NeoGeography: An extension of mainstream geography for everyone made by everyone? In Journal of Location Based Services (Vol. 3, Issue 2, pp. 75–81).</bibl>
            <idno type="DOI">10.1080/17489720903146824</idno>
          </bibl>
          <bibl n="139475">
            <bibl>Ratti, C., Baker, N., &amp;amp; Steemers, K. (2005). Energy consumption and urban texture. Energy and Buildings, 37(7), 762–776.</bibl>
            <idno type="DOI">10.1016/j.enbuild.2004.10.010</idno>
          </bibl>
          <bibl n="138423">
            <bibl>See, L., Estima, J., Pőd&amp;#246;r, A., Jokar Arsanjiani, J., Laso Bayas, J.-C., &amp;amp; Vatseva, R. (2017). Sources of VGI for Mapping. In Mapping and the Citizen Sensor (pp. 13–35). Ubiquity Press.</bibl>
            <idno type="DOI">10.5334/bbf.b</idno>
          </bibl>
          <bibl n="137734">
            <bibl>Vargas-Munoz, J. E., Srivastava, S., Tuia, D., &amp;amp; Falcao, A. X. (2021). OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing. IEEE Geoscience and Remote Sensing Magazine, 9(1), 184–199.</bibl>
            <idno type="DOI">10.1109/MGRS.2020.2994107</idno>
          </bibl>
        </listBibl>
      </div>
    </body>
  </text>
</TEI>