Robust optimal design of building cooling systems concerning uncertainties using mini-max regret theory

Various uncertainties exist in cooling systems at the plan and design stage. The conventional design method usually selects the cooling system or configurations without considering uncertainties, which may be risky because the performance of the cooling system may deviate from the expected, together with increased cost and reduced benefit due to uncertainties and variations in actual working conditions. A new, simple, and effective method is proposed to optimize the design of cooling systems and get the robust optimal cooling system by considering uncertainties in the information used at the design stage. The mini-max regret theory is used to realize the method. Two important problems in the design of cooling systems are studied: chiller combinations and chilled water pump configurations. By considering the uncertainties in the cooling load, the robust optimal chiller combination can be obtained. By considering the uncertainties in the resistance of chilled water pipelines, the robust optimal chilled water pump configuration can be determined.

[1]  Christina J. Hopfe,et al.  Uncertainty and sensitivity analysis in building performance simulation for decision support and design optimization , 2009 .

[2]  Marta M. B. Pascoal,et al.  The minmax regret robust shortest path problem in a finite multi-scenario model , 2014, Appl. Math. Comput..

[3]  Jose Manuel Cejudo-Lopez,et al.  Uncertainty in peak cooling load calculations , 2010 .

[4]  Bo An,et al.  Regret-Based Optimization and Preference Elicitation for Stackelberg Security Games with Uncertainty , 2014, AAAI.

[5]  Danny H.W. Li,et al.  Climatic effects on cooling load determination in subtropical regions , 2003 .

[6]  Ryohei Yokoyama,et al.  A revised method for robust optimal design of energy supply systems based on minimax regret criterion , 2014 .

[7]  Henrik Brohus,et al.  Quantification of uncertainty in predicting building energy consumption: A stochastic approach , 2012 .

[8]  William Y. Fowlkes,et al.  Engineering Methods for Robust Product Design: Using Taguchi Methods in Technology and Product Development , 1995 .

[9]  Daniel Vanderpooten,et al.  Min-max and min-max regret versions of combinatorial optimization problems: A survey , 2009, Eur. J. Oper. Res..

[10]  Tae Hee Lee,et al.  Robust Design: An Overview , 2006 .

[11]  Alessandro Prada,et al.  On the effect of material uncertainties in envelope heat transfer simulations , 2014 .

[12]  Bryan Eisenhower,et al.  Leveraging the analysis of parametric uncertainty for building energy model calibration , 2013 .

[13]  Igor Averbakh.  Minmax regret solutions for minimax optimization problems with uncertainty , 2000, Oper. Res. Lett..

[14]  Ery Djunaedy,et al.  Oversizing of HVAC system: Signatures and penalties , 2011 .

[15]  John E. Mottershead,et al.  A review of robust optimal design and its application in dynamics , 2005 .

[16]  Henrik Brohus,et al.  Application of sensitivity analysis in design of sustainable buildings , 2009 .

[17]  Yusuf Yildiz,et al.  Identification of the building parameters that influence heating and cooling energy loads for apartm , 2011 .

[18]  Haorong Li,et al.  Analysis of HVAC system oversizing in commercial buildings through field measurements , 2014 .

[19]  Robert C. Wolpert,et al.  A Review of the , 1985 .

[20]  Godfried Augenbroe,et al.  Exploring HVAC system sizing under uncertainty , 2014 .

[21]  Abraham Wald,et al.  Statistical Decision Functions , 1951 .

[22]  Ryohei Yokoyama,et al.  Multiobjective Optimal Design of Energy Supply Systems Based on Relative Robustness Criterion , 2002 .

[23]  Guohe Huang,et al.  An interval-valued minimax-regret analysis approach for the identification of optimal greenhouse-gas abatement strategies under uncertainty , 2011 .

[24]  Zheng O'Neill,et al.  Uncertainty and sensitivity decomposition of building energy models , 2012 .