Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design

作者：Yu, Wei^*; Li, Baizhan; Jia, Hongyuan; Zhang, Ming; Wang, Di

来源：Energy and Buildings, 2015, 88: 135-143.

DOI：10.1016/j.enbuild.2014.11.063

• Building design
• Energy consumption
• Thermal comfort
• Artificial neural network
• Multi-objective genetic algorithm

Several conflicting criteria exist in building design optimization, especially energy consumption and indoor environment thermal performance. This paper presents a novel multi-objective optimization model that can assist designers in green building design. The Pareto solution was used to obtain a set of optimal solutions for building design optimization, and uses an improved multi-objective genetic algorithm (NSGA-II) as a theoretical basis for building design multi-objective optimization model. Based on the simulation data on energy consumption and indoor thermal comfort, the study also used a simulation-based improved back-propagation (BP) network which is optimized by a genetic algorithm (GA) to characterize building behavior, and then establishes a GA-BP network model for rapidly predicting the energy consumption and indoor thermal comfort status of residential buildings; Third, the building design multi-objective optimization model was established by using the GA-BP network as a fitness function of the multi-objective Genetic Algorithm (NSGA-II); Finally, a case study is presented with the aid of the multi-objective approach in which dozens of potential designs are revealed for a typical building design in China, with a wide range of trade-offs between thermal comfort and energy consumption.

• 出版日期2015-2-1
• 单位重庆大学