<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>251</id>
    <completedYear>2017</completedYear>
    <publishedYear>2017</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>S. 2014 - 2023</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2017-12-20</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Simulation-time Reduction Techniques for a Retrofit Planning Tool</title>
    <abstract language="eng">The design of retrofitted energy efficient buildings is a promising option towards achieving a cost-effective improvement of the overall building sector’s energy performance. With the aim of discovering the best design for a retrofitting project in an automatic manner, a decision making (or optimization) process is usually adopted, utilizing accurate building Simulation models towards evaluating the candidate retrofitting scenarios. A major factor which affects the Overall computational time of such a process is the simulation execution time. Since high complexity and prohibitive simulation execution time are predominantly due to the full-scale, detailed simulation, in this work, the following simulation-time reduction methodologies are evaluated with respect to accuracy and computational effort in a test building: Hierarchical clustering; Koopman modes; and Meta-models. The simplified model that would be the outcome of these approaches, can be utilized by any optimization approach to discover the best retrofitting option.</abstract>
    <parentTitle language="eng">Proceedings of Building Simulation 2017: 15th Conference of IBPSA</parentTitle>
    <identifier type="isbn">978-1-7750520-0-5</identifier>
    <identifier type="url">http://www.ibpsa.org/?page_id=962</identifier>
    <identifier type="issn">2522-2708</identifier>
    <identifier type="urn">urn:nbn:de:bvb:92-opus4-2516</identifier>
    <note>Funding:&#13;
1. FP7-ICT-2011-6: Building as a Service (BaaS, #288409)&#13;
&#13;
2. H2020-EeB5-2015: Optimised Energy Efficient Design&#13;
Platform for Refurbishment at District Level (OptEEmAL, #680676)&#13;
&#13;
3. H2020-EeB-2015: Modelling Optimization of Energy Efficiency&#13;
in Buildings for Urban Sustainability (MOEEBIUS, #680517)</note>
    <enrichment key="ConferenceStatement">Building Simulation 2017, August 7-9, 2017, San Francisco, USA</enrichment>
    <licence>Creative Commons - Namensnennung-Keine kommerzielle Nutzung-Weitergabe unter gleichen Bedingungen</licence>
    <author>Georgios Giannakis</author>
    <author>Georgios Kontes</author>
    <author>Ivan Korolija</author>
    <author>Dimitrios Rovas</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Simulation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Retrofit Planning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Meta-models</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Hierarchical Clustering</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Gaussian Processes</value>
    </subject>
    <file>https://opus4.kobv.de/opus4-ohm/files/251/BS2017_554.pdf</file>
  </doc>
</export-example>
