Application of Multi-Objective Genetic Algorithm Based Simulation for Cost-Effective Building Energy Efficiency Design and Thermal Comfort Improvement

Building design following the energy efficiency standards may not achieve the optimal performance in terms of investment cost, energy consumption and thermal comfort. In this paper, an improved multi-objective genetic algorithm (NSGA-II) is combined with building simulation to assist building design optimization for five selected cities located in the hot summer and cold winter region in China. The trade-offs between the annual energy consumption and initial construction cost, as well as between life cycle cost and number of thermal discomfort hours, were explored. Sensitivity analysis of various design parameters on building energy consumption is performed. The optimizations predicted annual energy consumption reduction of 29.08% on average, as compared to a reference building designed following the standard, and 38.6% with 3.18% more cost on the initial investment. New values for a number of building design parameters are recommended for the revision of relevant building energy efficiency standard.

[1]  Sha Liu,et al.  Building information modeling based building design optimization for sustainability , 2015 .

[2]  D. Gossard,et al.  Multi-objective optimization of a building envelope for thermal performance using genetic algorithms and artificial neural network , 2013 .

[3]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[4]  Daisuke Sumiyoshi,et al.  Optimization of passive design measures for residential buildings in different Chinese areas , 2012 .

[5]  Ali F. Alajmi,et al.  Selecting the most efficient genetic algorithm sets in solving unconstrained building optimization problem , 2014 .

[6]  Essia Znouda,et al.  Optimization of Mediterranean building design using genetic algorithms , 2007 .

[7]  Salvatore Carlucci,et al.  Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design , 2013 .

[8]  Ala Hasan,et al.  Applying a multi-objective optimization approach for Design of low-emission cost-effective dwellings , 2011 .

[9]  Weimin Wang,et al.  Floor shape optimization for green building design , 2006, Adv. Eng. Informatics.

[10]  Francesco Causone,et al.  Multi-objective optimization of a nearly zero-energy building based on thermal and visual discomfort minimization using a non-dominated sorting genetic algorithm (NSGA-II) , 2015 .

[11]  Gerardo Maria Mauro,et al.  A new methodology for cost-optimal analysis by means of the multi-objective optimization of building energy performance , 2015 .

[12]  Di Wang,et al.  Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design , 2015 .

[13]  Moncef Krarti,et al.  Genetic-algorithm based approach to optimize building envelope design for residential buildings , 2010 .

[14]  Jae-Weon Jeong,et al.  Optimization of a free-form building shape to minimize external thermal load using genetic algorithm , 2014 .

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

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