Daylighting And Quality View Prediction For Atriums: LEED-IEQ-Demonstration Approach

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Daylighting And Quality View Prediction For Atriums: LEED-IEQDemonstration Approach Michal Gryko [1] Sammar Z. Allam[1] [3] Gabriella Rossi [2] , Hesham Shawky [1] , David Leon[1] 1Institute

for advanced Architecture of Catalonia, IAAC, UPC, Spain 2 CITA/ Royal Danish Academy 3, College of Architecture and Design, Effat University, Saudi Arabia

Keywords: Atrium Buildings, LEED-IEQ , Environmental Simulation, ANN, Kohonen Map

Abstract Atrium spaces globally form an integral part of many public buildings and have a great impact both psychologically and environmentally on the spaces they form. The ability to rapidly assess the physical form and environmental impact simultaneously while adhering to internationally recognized green building rating systems could prove to be invaluable in early-stage design. To fulfill indoor environmental quality (IEQ) requirements on green building rating systems like LEED, certain specifications are needed which can be time-consuming to calculate. Through the generation of datasets recording key geometric features of atriums and their relationship to environmental predictions including daylighting, thermal comfort and view quality, a database is created to allow Machine Learning algorithms to predict ratings more quickly than manual simulations. In this case, regression using artificial neural networks is used to predict the environmental output of a varied range of standard atrium types and as a result calculate what criteria is met to give points score. ANN is trained using ‘relu’ as an activation function, ‘Adam’ as optimizer, and ‘mean_squared_error’ as loss function. In combination with parametric modeling software like Grasshopper and live connections to machine learning libraries like Tensor Flow through plugins like Hops, a trained and simulated atrium model can provide instant LEED’s score as the atrium is configured by the designer. 1.

Introduction

In the realm of climate change and global warming the world is witnessing nowadays and has been persisting for the last decade, a holistic perspective of architectural design and building construction is essential to address the increasing demand in energy and address pragmatically green buildings’ design. Building construction energy consumption is 34% of the total energy pattern. Furthermore, carbon emissions resulting from buildings is almost one thirds of the total carbon emissions as stated by the Euspoean environment agency (EEA) .

Figure 1. LEED rating system intervention through design process phases Moreover, Artificial intelligence (AI) is a recently evolving tool which encompass performance prediction towards optimization. Consequently, demonstrating AI-based tools into buildings’ energy performance prediction is a beneficial instrument to refine buildings’ design through the design process. AI-aided green building design can predict energy consumption and carbon emissions of buildings prior its construction and support architects’ decision making to finetune design alternatives from a sustainable perspective.


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Daylighting And Quality View Prediction For Atriums: LEED-IEQ-Demonstration Approach by Michal Gryko - Issuu