Multi-criterion stochastic optimal selection of a double glazing system

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• 作者：Young-Jin Kim ; Cheol-Soo Park
• 关键词：Stochastic optimal design ; Multi ; criterion ; Gaussian Process emulator ; Pareto ; Decision making
• 刊名：Building Simulation
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：10
• 期：1
• 页码：1-9
• 全文大小：1,797 KB
• 刊物类别：Engineering
• 刊物主题：Building Construction, HVAC and Refrigeration
  Engineering Thermodynamics and Transport Phenomena
  Atmospheric Protection, Air Quality Control and Air Pollution
  Environmental Computing and Modeling
  Chinese Library of Science
  
• 出版者：Tsinghua University Press, co-published with Springer-Verlag GmbH
• ISSN：1996-8744
• 卷排序：10

文摘

In this paper, the authors present the multi-criterion stochastic optimal selection of a double glazing system for a given office building. Four elements are required for a multi-criterion stochastic optimal design: an accurate and fast simulation model, an optimization solver, correct handling of the multi-criterion decision problem, and consideration of the stochastic performance quantification. Since stochastic optimal design is a double-exponential problem, the Gaussian Process (GP) emulator, as a surrogate to EnergyPlus, was used to achieve computational efficiency. The GP emulator was derived based on the training set generated from EnergyPlus simulation runs. A genetic algorithm and Pareto optimality were then applied to deal with the multi-criterion optimization. Stochastic performance quantification was performed using a stochastic objective function and Latin Hypercube Samplings (LHS). Using the aforementioned four elements, the authors realized the multi-criterion stochastic optimal design of a double glazing system. The differences in the mean values of energy consumption and PMV between EnergyPlus and both GP emulators are 0.27 (kWh/m2) and 0.16 (kWh/m2), respectively, and 0.01 and 0.00, respectively. In addition, 13 non-dominated Pareto optimal solutions were successfully obtained. The approach presented in this paper improves computation efficiency for a multi-criterion stochastic optimal design problem and contributes to higher-fidelity simulation-based decision making.