期刊
ENERGIES
卷 10, 期 2, 页码 -出版社
MDPI
DOI: 10.3390/en10020245
关键词
building performance design; multi-objective optimization; residential building; algorithm comparison
资金
- National Natural Science Foundation of China [61304075, 61233006]
- Jiangsu Provincial Natural Science Foundation of China [BK20150525]
- China Postdoctoral Science Foundation [2016M601741, 2016M591784, 2016M600381]
- Jiangsu Provincial Postdoctoral Science Foundation of China [1601130B, 1501101B, 1601038C]
Numerous conflicting criteria exist in building design optimization, such as energy consumption, greenhouse gas emission and indoor thermal performance. Different simulation-based optimization strategies and various optimization algorithms have been developed. A few of them are analyzed and compared in solving building design problems. This paper presents an efficient optimization framework to facilitate optimization designs with the aid of commercial simulation software and MATLAB. The performances of three optimization strategies, including the proposed approach, GenOpt method and artificial neural network (ANN) method, are investigated using a case study of a simple building energy model. Results show that the proposed optimization framework has competitive performances compared with the GenOpt method. Further, in another practical case, four popular multi-objective algorithms, e.g., the non-dominated sorting genetic algorithm (NSGA-II), multi-objective particle swarm optimization (MOPSO), the multi-objective genetic algorithm (MOGA) and multi-objective differential evolution (MODE), are realized using the propose optimization framework and compared with three criteria. Results indicate that MODE achieves close-to-optimal solutions with the best diversity and execution time. An uncompetitive result is achieved by the MOPSO in this case study.
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