2018
DOI: 10.1016/j.aej.2018.04.006
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The parametric based optimization framework daylighting and energy performance in residential buildings in hot arid zone

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Cited by 80 publications
(31 citation statements)
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“…The strength Pareto evolutionary algorithm was introduced to achieve a compromise between energy consumption and indoor comfort. A. Toutou et al [48] explored the possibility of parametric modeling methods in the sustainable design of residential buildings. A variety of room fenestrations were tried, with EUI (energy use intensity) and SDA (spatial daylight autonomy) as evaluation indicators.…”
Section: Envelopesmentioning
confidence: 99%
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“…The strength Pareto evolutionary algorithm was introduced to achieve a compromise between energy consumption and indoor comfort. A. Toutou et al [48] explored the possibility of parametric modeling methods in the sustainable design of residential buildings. A variety of room fenestrations were tried, with EUI (energy use intensity) and SDA (spatial daylight autonomy) as evaluation indicators.…”
Section: Envelopesmentioning
confidence: 99%
“…Most of the published works (more than 80%) focus on the energy use of nonresidential buildings. Since the increase in housing units and the improvement of living standards, the energy consumption related to the residential construction sector has skyrocketed in the past decade [48,78,87]. Unfortunately, this grim fact still seems to have failed to attract attention.…”
Section: Current Status and Featuresmentioning
confidence: 99%
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“…The parametric simulation represents one of the most valid tools in the optimization process of building performance. This is true not only in the design phase: parametric modelling has drawn attention also as a potential solution with which to provide an intelligent searching method for efficient feedback [63]. For analysis related to electric lighting the software Dialux, which is also widely used in professional practice, was used.…”
Section: Case Study Simulationmentioning
confidence: 99%
“…The most efficient and strong chromosomes are selected in order to create the next population. The evolutionary principles of genetic algorithms allow the generation of well-performing instances and search for the solution closest to the optimal one in the given space [29]. In order to solve the optimization problem, the parameters and constraints of the problem should be identified.…”
Section: Introductionmentioning
confidence: 99%