Optimizing wall insulation material parameters in renovation projects using NSGA-II

Abstract:
Renovation        works        introduce        numerous        complexities that can only be addressed by those who excel in this specific  design  task.  Such  issues  as  energy  consumption,  which  requires  examination  of  excessive  alternatives,  is  not  of  primary  concern  through  the  design  process  due  further  time  limitations.  However,   computational   intelligence   methods   prove   to   be   valuable  decision  support  tools.  To  this  end,  the  current  study  aims  to  determine  optimum  wall  insulation  material  parameters  while     minimizing     optimization targets,     namely     energy     consumption  and  investment  costs.  To  accomplish,  first,  energy  model  of  an  actual  case,  located  in  the  province  of  Selçuk,  was  developed   using   OpenStudio   cross   platform.   Following,   54   simulations  were  run  to  generate  the  data  base  for  the  given  parameters   of   selected   insulation   alternatives.   Subsequently,   generated  data  base  was  employed  to  train  predictive  models  of  energy  generation  and  investment  costs.  Finally,  optimization  targets   were   minimized   using   NSGA-II   algorithm.   Results   rigorously demonstrate that NSGA-II was able to converge a set of non-dominated set of solutions. Further study will address the implementation  and  comparison  among  other  algorithms  for  the  problem under investigation.   

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