Parametric Approach for Multi-Objective Optimization for Daylighting and Energy Consumption in Early Stage Design of Office Tower in New Administrative Capital City of Egypt

Ahmed Mohamed Yousef Toutou
In the last few years, great improvements have been achieved in building optimization methods. Mustapha Sadeghipour Roudasri and others found new tools ” Ladybug, Honeybee and Butterfly” which could gather many simulation engines and visualization tools ” Energyplus, OpenStudio, Radiance, Daysim, CFD, OpenFOAM, etc ”. Consequently, These simulation engines will integrate with parametric modeling in Grasshopper and multiobjective optimization through Octopus plug-in to form an early stage parametric optimization framework in one canvas. This paper aims at finding the suitable plane shape and building configurations for multi-objective optimization to the daylighting levels and energy consumption of office tower building in the new administrative capital city in Egypt through parametric based optimization method. One of the most commonly used plan shapes of these types of buildings was studied. This shape and many building configurations ”WWR, window material, wall material and shading devices” were parametrically modeled. These Parameters will form many tradeoffs which will be simulated and optimized by the previous framework. Spatial Daylight Autonomy ”SDA300/50%” is examined to optimize Daylighting while Energy Use Intensity ” EUI” is used for energy consumption optimization. Multi-Objective Optimization was performed by genetic algorithms via Octopus plug-in. The near optimum design for plan shape and building configuration to balance between daylighting and energy consumption is achieved and will be a reference model for office tower buildings in this zone in Egypt which is under rapid development. The framework used in this study will guide designers to find effective solutions for early-stage design of office building in one canvas without any conflict between several engines and scripts.

Keywords

Parametric Optimization; Genetic Algorithm; Daylighting; Energy Simulation; Early Stage Design

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