Reference : Assessing gaps and needs for integrating building performance optimization tools in n...
Scientific journals : Article
Engineering, computing & technology : Architecture
http://hdl.handle.net/2268/163817
Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design
English
Attia, Shady mailto [Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland > School of Architecture, Civil and Environmental Engineering (ENAC) > Interdisciplinary Laboratory of Performance-Integrated Design (LIPID) > 2013 >]
Hamdy, Mohamed [Aalto University, Finland > School of Engineering > Department of Energy Technology > 2013 >]
O’Brien, William [Carlton University, Canada > Department of Building and Civil & Environmental Engineering > > 2013 >]
Carlucci, Salvatore [Politecnico di Milano, Italy > Dipartimento di Energia > > 2013 >]
May-2013
Energy and Buildings
Elsevier
60
110-124
Yes (verified by ORBi)
International
0378-7788
[en] Simulation-based optimization ; Zero energy buildings ; Evolutionary algorithms ; Needs ; Gaps ; Review ; Interview
[en] This paper summarizes a study undertaken to reveal potential challenges and opportunities for integrating optimization tools in net zero energy buildings (NZEB) design. The paper reviews current trends in simulation-based building performance optimization (BPO) and outlines major criteria for optimization tools selection and evaluation. This is based on analyzing user's needs for tools capabilities and requirement specifications. The review is carried out by means of a literature review of 165 publications and interviews with 28 optimization experts. The findings are based on an inter-group comparison between experts. The aim is to assess the gaps and needs for integrating BPO tools in NZEB design. The findings indicate a breakthrough in using evolutionary algorithms in solving highly constrained envelope, HVAC and renewable optimization problems. Simple genetic algorithm solved many design and operation problems and allowed measuring the improvement in the optimality of a solution against a base case. Evolutionary algorithms are also easily adapted to enable them to solve a particular optimization problem more effectively. However, existing limitations including model uncertainty, computation time, difficulty of use and steep learning curve. Some future directions anticipated or needed for improvement of current tools are presented.
http://hdl.handle.net/2268/163817
10.1016/j.enbuild.2013.01.016
http://www.sciencedirect.com/science/article/pii/S0378778813000339

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