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

doi:10.1016/j.enbuild.2014.11.063

Cited by 339 publications

(137 citation statements)

References 15 publications

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“…The GA generates inputs to the building simulation software, while the later return simulation results on energy consumption, cost and return the values back to GA for optimization. Selection of the inputs are based on the findings from open literature (Wang et al, 2006;Dubrow and Krarti, 2010;Hamdy et al, 2011;Zhang et al, 2011;Gong et al, 2012;Jin and Jeong, 2014;Ascione et al, 2015;Liu et al, 2015;Yu et al, 2015). The design parameters selected in this paper are related to building orientation, WWR, window shading, heating temperature set point, cooling temperature set point, external wall structure (insulation), roof structure (insulation), and glazing type.…”

Section: Methodology Optimization Frameworkmentioning

confidence: 99%

“…Other researchers have considered energy consumption and thermal comfort (Wright et al, 2002;Gossard et al, 2013;Carlucci et al, 2015;Yu et al, 2015). Wright et al (2002) applied multi-objective genetic algorithms (MOGA) approach to find the optimum pay-off characteristic between daily operating energy cost and thermal discomfort with emphasis on HVAC system design.…”

Section: Introductionmentioning

confidence: 99%

“…Gossard et al (2013) coupled artificial neural network with the non-dominated-and-crowding sorting genetic algorithm II (NSGA-II) to optimize the equivalent thermos physical properties of the external walls to reduce the annual energy consumption (AEC) and improve thermal comfort in summer. Yu et al (2015) coupled back-propagation network with NSGA-II to improve thermal comfort and reduce energy consumption. Carlucci et al (2015) applied NSGA-II to find the optimum K values of external walls, roof, floor, and windows as well as shading and window type to improve indoor thermal and visual comfort.…”

Section: Introductionmentioning

confidence: 99%

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Application of Multi-Objective Genetic Algorithm Based Simulation for Cost-Effective Building Energy Efficiency Design and Thermal Comfort Improvement

Lin

Wang

2018

Front. Energy Res.

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Building design following the energy efficiency standards may not achieve the optimal performance in terms of investment cost, energy consumption and thermal comfort. In this paper, an improved multi-objective genetic algorithm (NSGA-II) is combined with building simulation to assist building design optimization for five selected cities located in the hot summer and cold winter region in China. The trade-offs between the annual energy consumption (AEC) and initial construction cost, as well as between life cycle cost (LCC) and number of thermal discomfort hours, were explored. Sensitivity analysis of various design parameters on building energy consumption is performed. The optimizations predicted AEC reduction of 29.08% on average, as compared to a reference building designed following the standard, and 38.6% with 3.18% more cost on the initial investment. New values for a number of building design parameters are recommended for the revision of relevant building energy efficiency standard.

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Section: Methodology Optimization Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of Multi-Objective Genetic Algorithm Based Simulation for Cost-Effective Building Energy Efficiency Design and Thermal Comfort Improvement

Lin

Wang

2018

Front. Energy Res.

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“…In the selection step, certain individuals of the population are extracted that will generate the next generation. The crossover operator is applied to a pair of selected population members to create the next offspring, and the mutation operator is used as a slight modification of this offspring, or of the remaining members of the population [25].…”

Section: Genetic Algorithmmentioning

confidence: 99%

Structure Optimization of Stand-Alone Renewable Power Systems Based on Multi Object Function

2016

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This paper presents a methodology for the size optimization of a stand-alone hybrid PV/wind/diesel/battery system while considering the following factors: total annual cost (TAC), loss of power supply probability (LPSP), and the fuel cost of the diesel generator required by the user. A new optimization algorithm and an object function (including a penalty method) are also proposed; these assist with designing the best structure for a hybrid system satisfying the constraints. In hybrid energy system sources such as photovoltaic (PV), wind, diesel, and energy storage devices are connected as an electrical load supply. Because the power produced by PV and wind turbine sources is dependent on the variation of the resources (sun and wind) and the load demand fluctuates, such a hybrid system must be able to satisfy the load requirements at any time and store the excess energy for use in deficit conditions. Therefore, reliability and cost are the two main criteria when designing a stand-alone hybrid system. Moreover, the operation of a diesel generator is important to achieve greater reliability. In this paper, TAC, LPSP, and the fuel cost of the diesel generator are considered as the objective variables and a hybrid teaching-learning-based optimization algorithm is proposed and used to choose the best structure of a stand-alone hybrid PV/wind/diesel/battery system. Simulation results from MATLAB support the effectiveness of the proposed method and confirm that it is more efficient than conventional methods.

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“…Such a manual procedure is very time-consuming using CFD simulations, and is often impractical for more than two or three independent variables. Researchers [7][8][9][10][11] also combined data-driven methods with CFD to relieve computing burden. Zhou and Haghighat [7,8] applied artificial neural networks (ANNs) in place of CFD simulation inside genetic algorithm (GA) search loops to improve thermal comfort and IAQ without sacrificing energy costs of ventilation systems in office buildings.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Environment/Energy Pareto Optimal Front of an Office Room Using Computational Fluid Dynamics-Based Interactive Optimization Method

Xue

2017

Energies

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This paper is concerned with the development of a high-resolution and control-friendly optimization framework in enclosed environments that helps improve thermal comfort, indoor air quality (IAQ), and energy costs of heating, ventilation and air conditioning (HVAC) system simultaneously. A computational fluid dynamics (CFD)-based optimization method which couples algorithms implemented in Matlab with CFD simulation is proposed. The key part of this method is a data interactive mechanism which efficiently passes parameters between CFD simulations and optimization functions. A two-person office room is modeled for the numerical optimization. The multi-objective evolutionary algorithm-non-dominated-and-crowding Sorting Genetic Algorithm II (NSGA-II)-is realized to explore the environment/energy Pareto front of the enclosed space. Performance analysis will demonstrate the effectiveness of the presented optimization method.

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

Cited by 339 publications

References 15 publications

Application of Multi-Objective Genetic Algorithm Based Simulation for Cost-Effective Building Energy Efficiency Design and Thermal Comfort Improvement

Application of Multi-Objective Genetic Algorithm Based Simulation for Cost-Effective Building Energy Efficiency Design and Thermal Comfort Improvement

Structure Optimization of Stand-Alone Renewable Power Systems Based on Multi Object Function

Exploring the Environment/Energy Pareto Optimal Front of an Office Room Using Computational Fluid Dynamics-Based Interactive Optimization Method

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