Methodology of evolutionary selection in energy efficient architecture
Role of genetic algorithm in calculating the architectural spaces energy efficiency
The growing demand to energy impacted negatively on environment as result of burning fossil fuel, and because buildings are main energy consumer, studies have been called towards creating efficient buildings, In which digital simulation technology, presented with designer to select efficient design solutions, which is uneasy task, due to the large number of models to be evaluated by simulation, consuming time and effort, due to large number of design variables interacting and conflicting in influence. Limiting the effectiveness of (traditional computer simulation approach). And Emersion (genetic algorithm approach) as an effective alternative to assess and optimize design, by automated steps for exchanging design variables between efficient models and produce more efficient new models, away similar to genetic evolution of organisms. But, lake of what and how to adopting this approach limited its use in local architectural researches and practices, Highlighting a problem: "Lack of cognitive clarity about the genetic algorithms approach in creating high-efficiency domestic compared to traditional approach”, aiming to “determining the nature of the genetic algorithm approach and its application to create accurate and high efficiency domestic designs in less time and effort comparing to the traditional approach”. Research methodology was reviewing genetic algorithm mechanism in solving design problems, making a comparison between traditional simulation approach and genetic algorithm approach on virtual model within local environment, determining the most efficient approach which balances characteristics (windows to wall ratio) and (perfect orientation) to provide the highest rates of natural lighting with minimum solar thermal gain, as well as the time it takes to complete the work according to each of the two approaches. Reaching a number of conclusions about effectiveness to adopt genetic algorithm approach in terms of time reduction and results accuracy compared to the traditional approach.
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