Multi-Objective Optimization for Energy Performance Improvement of Residential Buildings: A Comparative Study

Kangji Li, Lei Pan, Wenping Xue, Hui Jiang and Hanping Mao
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Kangji Li: School of Electricity Information Engineering, Jiangsu University, Zhenjiang 212013, China
Lei Pan: School of Electricity Information Engineering, Jiangsu University, Zhenjiang 212013, China
Wenping Xue: School of Electricity Information Engineering, Jiangsu University, Zhenjiang 212013, China
Hui Jiang: School of Electricity Information Engineering, Jiangsu University, Zhenjiang 212013, China
Hanping Mao: Institute of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China

Energies, 2017, vol. 10, issue 2, 1-23

Abstract: 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.

Keywords: building performance design; multi-objective optimization; residential building; algorithm comparison (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

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