Abstract

The physical characteristics of high-rise buildings are among the complex and influential factors that contribute to energy consumption reduction. This research introduces an algorithmic method to investigate the impact of volumetric porosity of buildings on the level of cooling and heating load consumption. The objective of this research is to elucidate the correlation between the aforementioned variables in a high-rise residential complex in Tehran city through a proposed method. This objective was accomplished through the creation of a parametric and variable “Modular Generative Model”. To this end, a parametric generative model was utilized to extract models with varying porosity percentages. These models were then influenced by the percentage of surface radiation in Tehran on two specific dates: the summer solstice and the winter solstice. The extraction process was performed algorithmically. Then, a multi-parametric simulation was conducted on these two dates, considering various parameters, in a way that can be generalized to other global climates. The correlation between the pore volume percentage and the energy consumption was calculated and tested on the aforementioned dates.In the subsequent phase, energy simulation was carried out once again to optimize energy consumption for a specific climate. This involved replacing the glass modules with porous modules in the mentioned samples and leveraging their greenhouse effect. Subsequently, the correlation between the greenhouse effect and the cooling and heating load consumption was analyzed by studying the correlation graph. Finally, in order to achieve the optimal solution, a genetic algorithm was employed to determine the optimal values of the independent variables, which would result in the minimum values of the dependent variables. The findings of this research provide a set of quantitative recommendations based on the most optimal solution, which can be utilized as form-finding rules in the initial stages of design.

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