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Energies 2017, 10(2), 245; doi:10.3390/en10020245

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

1
School of Electricity Information Engineering, Jiangsu University, Zhenjiang 212013, China
2
Institute of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
*
Author to whom correspondence should be addressed.
Academic Editors: Yacine Rezgui and Monjur Mourshed
Received: 1 January 2017 / Revised: 30 January 2017 / Accepted: 14 February 2017 / Published: 17 February 2017
(This article belongs to the Special Issue Zero-Carbon Buildings)
View Full-Text   |   Download PDF [2675 KB, uploaded 17 February 2017]   |  

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. View Full-Text
Keywords: building performance design; multi-objective optimization; residential building; algorithm comparison building performance design; multi-objective optimization; residential building; algorithm comparison
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Li, K.; Pan, L.; Xue, W.; Jiang, H.; Mao, H. Multi-Objective Optimization for Energy Performance Improvement of Residential Buildings: A Comparative Study. Energies 2017, 10, 245.

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