Computational Multivariable Optimization Of Indoor Daylighting And Glare In Architectural Design

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Buildings with daylighting problems may lead to compromised psychological and physiological wellbeing, and decreased productivity of occupants. Although there exists some research on design optimization for enhanced daylight and glare performance of buildings, most studies considered single design variable in the optimization. In this study, a computational multivariable optimization framework is developed for enhanced interior daylighting and glare performance of buildings. The proposed framework is derived considering the influence of (a) building orientation and (b) facade opening. The suitability of the proposed optimization framework is assessed through implementation on a sample office space and a selected case study building. To ensure the accuracy of the computational results, the illuminance level obtained from the simulations are compared with that of on-site measurements taken using Lux meter. The performance of the proposed framework is evaluated in terms of the acceptable values of annual sunlight exposure (ASE) and spatial daylight autonomy (sDA). For the sample office space, multiple optimized design solutions that satisfy the criteria specified by Illuminating Engineering Society (IES) and Leadership in Energy and Environmental Design (LEED) are obtained using the proposed framework. The application of the framework on case study building, based on the existing facade design, has provided several optimized solutions. The solutions have resulted in a significant reduction of glare/ASE (i.e., up to 12% reduction) without significant compromise in the daylighting/sDA (i.e., 0.8% reduction). Therefore, it is concluded that the proposed framework has the potential to provide multiple design options that could achieve optimized daylight and glare performance.

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Computational Multivariable Optimization Of Indoor Daylighting And Glare In Architectural Design

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