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Progress of Sanne Hettinga

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May 31, 2016
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Progress of Sanne Hettinga

Avatar for 3D4EM

3D4EM

May 31, 2016

Transcript

  1. Outline — Overview of PhD progress — Cooperation with Den

    Haag on insulation, GIS and BIM — Cooperation on sustainable energy planning — Demo by Sanne — Infrastructure by Steven Fruijtier – Geodan — Cooperation on serious gaming and energy planning — Game interface by Steven Fruijtier – Geodan — Game dynamics by Sanne — Future research topics — Plan compiling from Minecraft results — Perceived added value of 3D in flood crisis management
  2. How do we do it today? — Average gas consumption

    is 1440 m3 — Energy Agreement (2013-2023) — Social housing label B by 2023 — Privately owned housing label C by 2023 — Need to insulate smart and efficient — Make specific plan for individual houses and optimize solutions
  3. What can GIS and BIM contribute GIS BIM — Building

    geometry — Volume — Etc. — Weather conditions — LOD4 — Unheated inner spaces — Building materials Bron: www.lont.nl Bron: www. 3d.bk.tudelft.nl
  4. Wat is it? 1. Energy system & context Presenting information

    giving insight in the area Evaluating the issues and possibilities Collaboration Designing interventions & identifying scenarios Impact analysis of designs Review impact Collaboration to reach decision 2. Processes 3. Issues & possibilities 4. Scenarios 5. Impact 6. Decision making Presenting information raising awareness A Step Interaction Tools
  5. Game rules — 3 technologies — Wind — Solar panels

    — Insulation — 3 indicators — Investment cost — Energy generated/saved — CO2 emissions avoided — 1 score — 28 groups of 3 students battling for the best plan
  6. Demo of further development — In game scoring — Different

    separate models — Integrating better models — More advanced game mechanics
  7. 3D flood modelling for crisis management — Perceived added value

    analysis — Psychological added value — 3D viewer, using 3D flood data, 3D flood model — Case study — Ask subjects using questionnaires — Do they have better comprehension of the situation? — Can they plan mitigation better? — Can they train more efficient? — Etc.
  8. Minecraft plan compiling — Currently I have 28 plans —

    I can select best plan — Best financial — Best economic — Etc. — However I want to compile 1 best plan from all plans — Using genetic algorithms — Finding the proper grid size — Using this methodology local government can summarize large public input in a coherent plan