Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design - Yu, Wei; Li, Baizhan; Jia, Hongyuan; Zhang, Ming; Wang, Di (Energy and buildings, 2015)
•We present a multi-objective optimization model that assist green building design.•We use an improved multi-objective genetic algorithm (NSGA-II) as theory basis.•We present a case study with the aid of the multi-objective approach. 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.