Surrogate modeling for the fast optimization of energy systems

While software simulations in the building technology field are essential for designing efficient systems, for specific applications, the tools currently available are computationally too expensive. This paper presents a novel approach for the fast computation of accurate simulation results and demonstrates its efficacy for solving a multiobjective optimization problem.

[1]  Christian Inard,et al.  Fast method to predict building heating demand based on the design of experiments , 2009 .

[2]  Magnus Wahlström,et al.  A New Randomized Algorithm to Approximate the Star Discrepancy Based on Threshold Accepting , 2012, SIAM J. Numer. Anal..

[3]  John W. Bandler,et al.  Editorial—surrogate modeling and space mapping for engineering optimization , 2008 .

[4]  Kyoung-Ho Lee,et al.  Preliminary determination of optimal size for renewable energy resources in buildings using RETScreen , 2012 .

[5]  Oliver Deussen,et al.  Farthest-point optimized point sets with maximized minimum distance , 2011, HPG '11.

[6]  Raffaele Bornatico,et al.  Optimal sizing of a solar thermal building installation using particle swarm optimization , 2012 .

[7]  A. Owen,et al.  Control variates for quasi-Monte Carlo , 2005 .

[8]  Hoseyn Sayyaadi,et al.  Design and optimization of a non-TEMA type tubular recuperative heat exchanger used in a regenerative gas turbine cycle , 2010 .

[9]  Ares Lagae,et al.  A Comparison of Methods for Generating Poisson Disk Distributions , 2008, Comput. Graph. Forum.

[10]  R. Franke.  Scattered data interpolation: tests of some methods , 1982 .

[11]  Maria Cristina Munari Probst,et al.  Simulation Tool for Architects , 2009 .

[12]  Bruno Lévy,et al.  Geometry-aware direction field processing , 2009, TOGS.

[13]  Steve C. Maddock,et al.  Accurate multidimensional Poisson-disk sampling , 2009, TOGS.

[14]  Xing Shi.  Design optimization of insulation usage and space conditioning load using energy simulation and gene , 2011 .

[15]  Li-yi Wei,et al.  Differential domain analysis for non-uniform sampling , 2011, SIGGRAPH 2011.

[16]  Poul Alberg Østergaard,et al.  Reviewing optimisation criteria for energy systems analyses of renewable energy integration , 2009 .

[17]  Shady Attia,et al.  Selection criteria for building performance simulation tools: contrasting architects' and engineers' needs , 2012 .

[18]  François Maréchal,et al.  Methods for multi-objective investment and operating optimization of complex energy systems , 2012 .

[19]  I. Taymaz,et al.  Application of response surface methodology to optimize and investigate the effects of operating conditions on the performance of DMFC , 2011 .

[20]  Eckart Zitzler,et al.  Evolutionary algorithms for multiobjective optimization: methods and applications , 1999 .

[21]  Wolfgang Heidrich,et al.  Fast Primitive Distribution for Illustration , 2002, Rendering Techniques.

[22]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[23]  Bryan A. Tolson,et al.  Review of surrogate modeling in water resources , 2012 .

[24]  Joachim Pouderoux,et al.  Adaptive hierarchical RBF interpolation for creating smooth digital elevation models , 2004, GIS '04.

[25]  Pradeep Sen,et al.  Efficient Computation of Blue Noise Point Sets through Importance Sampling , 2011, EGSR '11.

[26]  A. Blokhuis.  SPHERE PACKINGS, LATTICES AND GROUPS (Grundlehren der mathematischen Wissenschaften 290) , 1989 .

[27]  Jon Hand,et al.  CONTRASTING THE CAPABILITIES OF BUILDING ENERGY PERFORMANCE SIMULATION PROGRAMS , 2008 .

[28]  Don P. Mitchell,et al.  Generating antialiased images at low sampling densities , 1987, SIGGRAPH.

[29]  Robert L. Cook,et al.  Stochastic sampling in computer graphics , 1988, TOGS.

[30]  N. J. A. Sloane,et al.  Sphere Packings, Lattices and Groups , 1987, Grundlehren der mathematischen Wissenschaften.

[31]  J. Yellott.  Spectral consequences of photoreceptor sampling in the rhesus retina. , 1983, Science.

[32]  Robert Ulichney,et al.  Digital Halftoning , 1987 .

[33]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[34]  Hoseyn Sayyaadi,et al.  Efficiency enhancement of a gas turbine cycle using an optimized tubular recuperative heat exchanger , 2012 .

[35]  H. Niederreiter.  Quasi-Monte Carlo methods and pseudo-random numbers , 1978 .

[36]  Soteris A. Kalogirou,et al.  Designing and Modeling Solar Energy Systems , 2009 .

[37]  Bert de Vries,et al.  A global model for residential energy use: Uncertainty in calibration to regional data , 2010 .

[38]  Mads Pagh Nielsen,et al.  A cost optimization model for 100% renewable residential energy supply systems , 2012 .

[39]  K. Vajen,et al.  INFLUENCE OF THE DHW-LOAD PROFILE ON THE FRACTIONAL ENERGY SAVINGS: A CASE STUDY OF A SOLAR COMBI-SYSTEM WITH TRNSYS SIMULATIONS , 2001 .

[40]  M. S. Bartlett,et al.  An introduction to stochastic processes, with special reference to methods and applications , 1955 .

[41]  Mark A. Z. Dippé,et al.  Antialiasing through stochastic sampling , 1985, SIGGRAPH.

[42]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[43]  Christian Ghiaus,et al.  Optimization of multifunction multi-source solar systems by design of experiments , 2012 .

[44]  Manuel E. Cruz,et al.  Response surface method applied to the thermoeconomic optimization of a complex cogeneration system modeled in a process simulator , 2013 .

[45]  M. Collares-Pereira,et al.  Simple procedure for generating sequences of daily radiation values using a library of Markov transition matrices , 1988 .