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

•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...

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Bibliographic Details
Published in:Energy and buildings Vol. 88; pp. 135 - 143
Main Authors: Yu, Wei, Li, Baizhan, Jia, Hongyuan, Zhang, Ming, Wang, Di
Format: Journal Article
Language:English
Published: Elsevier B.V 01.02.2015
Subjects:
Artificial neural network
Building design
Computer simulation
Construction
Design of buildings
Energy consumption
Genetic algorithms
Mathematical models
Multi-objective genetic algorithm
Networks
Optimization
Thermal comfort
China, People's Rep
Multi-objective genetic algorithm
Energy consumption
Thermal comfort
Artificial neural network
Building design
ISSN:0378-7788
Online Access:Get full text
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Summary:•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.
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ISSN:0378-7788
DOI:10.1016/j.enbuild.2014.11.063