Optimization of Mediterranean building design using genetic algorithms

Znouda, Essia; Ghrab-Morcos, Nadia; Hadj-Alouane, Atidel B.

doi:10.1016/j.enbuild.2005.11.015

Cited by 115 publications

(40 citation statements)

References 8 publications

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“…and Norford L.K., 2002; Znouda E. et al, 2007). One of the most common methodologies involves the continuous exchange of data between the simulation and the optimization models, where the first carries out the task of calculating the energy performance of the building, and therefore the values assumed by the objective function, while the second identify the best technological and constructive solutions.…”

Section: Flowchart Of Working Methodologymentioning

confidence: 99%

Implementation of a genetic algorithm for energy design optimization of livestock housing using a dynamic thermal simulator

Menconi¹,

Chiappini²,

Grohmann³

2013

J Agricult Engineer

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A Genetic Algorithm (GA) is an optimization process inspired by natural systems ability of surviving in many different environments through the mechanisms of natural selection and genetics. The pairing of GA-based optimization techniques with dynamic energy models is a common and effective practice to find energy efficient design solutions. In this paper is implemented an optimization tool that use a GA and a dynamic energy model. Efficiency of GAs depends largely on the coding strategy and on the parameters selection. In order to test the code and to find the best combination of parameters, a parametric analysis of GA's performances is carried out. The algorithm, coded in Matlab, works with populations of strings. Each string, that represents a complete design solution, is initially randomly generated by the GA and evaluated in terms of energy performances by the dynamic thermal simulator. A new population is then generated using three different GA stochastic operators: reproduction, crossover and mutation, by selecting, mixing and randomly modifying the fittest solutions of the previous generation. Each generation is energetically evaluated and thus the fitness of the strings, that represent the energy efficiency of the design solutions, improves every cycle till eventually converge to the best solution. This whole methodology is well documented and applied in residential buildings design but can be easily extended to livestock housing. In this paper the algorithm is coded to be applied on a simple sheepfold model in order to optimize only passive design solutions.

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Section: Flowchart Of Working Methodologymentioning

confidence: 99%

Implementation of a genetic algorithm for energy design optimization of livestock housing using a dynamic thermal simulator

Menconi¹,

Chiappini²,

Grohmann³

2013

J Agricult Engineer

View full text Add to dashboard Cite

show abstract

“…Bearing in mind a great number of variables which can be combined we might end up facing a vast number of combinations while the building itself will not be so complex. It is of vital importance to choose the right tool to solve such a complex problem [29].…”

Section: Optimization Algorithmmentioning

confidence: 99%

“…Although GAs do not guarantee finding the global optimum, they have become advanced optimization tool for a wide range of problems. GAs were further applied in a number of the papers quoted in the introduction of this paper [2,5,14,15,[29][30][31][32][33]35,36,38,[40][41][42].…”

Section: Optimization Algorithmmentioning

confidence: 99%

“…It is evident, for example, that an insulated building envelope helps in reducing heating demand. But in summer, the outdoor night temperature being lower than the required indoor temperature, uninsulated walls allow leading to the reduction of air-conditioning need [29]. Therefore, in climates where both heating and cooling throughout the year are required, the optimization of the design parameters in buildings should take into account the cost of both heating and cooling.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Variable Optimization of Building Thermal Design Using Genetic Algorithms

Ferdyn-Grygierek

Grygierek

2017

Energies

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Abstract:The building sector is one of the largest energy consumers in the world, comprising about 40% of the total energy consumption in numerous countries. Early design decisions have a significant impact on the energy performance of buildings. The paper presents the multi-variable optimization of the selected design parameters in a single-family building in temperate climate conditions. The influence of four types of windows, their size, building orientation, insulation of external wall, roof and ground floor and infiltration on the life cycle costs (LCC) is analyzed. Optimal selection of the design parameters is carried out using genetic algorithms by coupling the building performance simulation program EnergyPlus with optimization environment. The simulations were conducted for seven optimization cases. The analysis is performed for two variants of a building with heating and cooling systems and with a heating system only. Depending on the analyzed case, the life cycle costs decreased from 7% to 34% LCC value of the reference building. In the case of temperate climate, the building optimization (in terms of heat demand only) substantially reduces the heating costs, yet the summer thermal comfort conditions deteriorate significantly.

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“…For that reason designers should take into account the best combination of these parameters, which can minimize energy use over the lifespan of a building. Taking into account the number of variables that can be combined, we have an enormous number of combinations, even in the case of a not very complicated building [19]. Therefore the selection of a suitable optimization tool is very important.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of the Envelope of Building with Natural Ventilation

Grygierek

Ferdyn-Grygierek

2018

Energies

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Abstract:A properly designed house should provide occupants with the high level of thermal comfort at low energy demand. On many occasions investors choose to add additional insulation to the buildings to reduce heat demand. This may lead to overheating of the building without a cooling system in summer periods (these prevail in Poland). Additionally, it affects the deterioration of thermal comfort, which can only be improved by increasing ventilation. The paper presents the multi-objective optimization of the selected design parameters in a single-family building in temperate climate conditions. The influence of four types of windows, their size, building orientation, insulation of external wall, roof and ground floor and infiltration on the life cycle costs and thermal comfort is analyzed for the building without cooling. Infiltration changes during the simulation and is controlled by a special controller. Its task is to imitate the behavior of occupants in changing the supply airflow. Optimal selection of the design parameters is carried out using Non-dominated Sorting Genetic Algorithm II (NSGA-II) by coupling the building performance simulation program EnergyPlus with optimization environment. For the single-family house, optimal values of design variables for three different criteria are presented.

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Optimization of Mediterranean building design using genetic algorithms

Cited by 115 publications

References 8 publications

Implementation of a genetic algorithm for energy design optimization of livestock housing using a dynamic thermal simulator

Implementation of a genetic algorithm for energy design optimization of livestock housing using a dynamic thermal simulator

Multi-Variable Optimization of Building Thermal Design Using Genetic Algorithms

Multi-Objective Optimization of the Envelope of Building with Natural Ventilation

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