Multi-criterion stochastic optimal selection of a double glazing system

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.

[1]  Aki Vehtari,et al.  GPstuff: Bayesian modeling with Gaussian processes , 2013, J. Mach. Learn. Res..

[2]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[3]  Brent Griffith,et al.  Modeling Windows in Energy Plus with Simple Performance Indices , 2009 .

[4]  Zheng O'Neill,et al.  A methodology for meta-model based optimization in building energy models , 2012 .

[5]  Mohammed Alfaki.  Improving Efficiency in Parameter Estimation Using the Hamiltonian Monte Carlo Algorithm , 2008 .

[6]  Boaz Golany,et al.  Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions , 1985 .

[7]  Gary B. Lamont,et al.  Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art , 2000, Evolutionary Computation.

[8]  Wenyuan Li,et al.  Reliability Assessment of Electric Power Systems Using Monte Carlo Methods , 1994 .

[9]  Jonathan A. Wright,et al.  Optimization of building thermal design and control by multi-criterion genetic algorithm , 2002 .

[10]  Godfried Augenbroe,et al.  Local vs. integrated control strategies for double-skin systems , 2013 .

[11]  Carlos A. Coello Coello,et al.  Evolutionary multi-objective optimization: a historical view of the field , 2006, IEEE Comput. Intell. Mag..

[12]  Stefano Tarantola,et al.  Sensitivity Analysis in Practice , 2002 .

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

[14]  M. S. De Wit,et al.  Uncertainty in predictions of thermal comfort in buildings , 2001 .

[15]  Fariborz Haghighat,et al.  Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network , 2010 .

[16]  F. Hemez,et al.  Identification of response surface models using genetic programming , 2006 .

[17]  Wei Tian,et al.  A probabilistic energy model for non-domestic building sectors applied to analysis of school buildings in greater London , 2012 .

[18]  Matthew Brown,et al.  Kernel regression for real-time building energy analysis , 2012 .

[19]  Ala Hasan,et al.  Impact of adaptive thermal comfort criteria on building energy use and cooling equipment size using , 2011 .

[20]  Refrigerating.  ASHRAE handbook of fundamentals , 1967 .

[21]  Huafen Hu,et al.  Risk-conscious design of off-grid solar energy houses , 2009 .

[22]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[23]  Pieter de Wilde,et al.  Predicting the performance of an office under climate change: A study of metrics, sensitivity and zonal resolution , 2010 .