Synthetic Machine Learning For Real-Time Architectural Daylighting Prediction

Rutvik Deshpande Digital Blue Foam
Maciej Nisztuk Digital Blue Foam
Cesar Cheng Digital Blue Foam
Ramanathan Subramanian Digital Blue Foam
Tejas Chavan Digital Blue Foam
Camiel Weijenberg Digital Blue Foam
Sayjel Vijay Patel Digital Blue Foam

“Synthetic Machine Learning” offers a revolutionary leap in real-time environmental analysis for conceptual architectural design. By integrating automatic synthetic data generation, artificial neural network (ANN) training and online deployment, Synthetic Machine Learning offers two main advantages over conventional simulation; First, it reduces the analysis time for a reference simulation from minutes to seconds; Second, it is possible to deploy ANN as a web service in an online design environment, which therein increases accessibility, significantly reducing simulation costs and setup time. The application of Synthetic Machine Learning to perform Daylight Autonomy (DA) and Spatial Daylight Autonomy (sDA) studies to maximise building daylighting for a given use, window to wall ratio, and floorplan arrangement is showcased through a preliminary demonstration work. Comparatively the use of algorithmically generated synthetic data versus real-world data is becoming ubiquitous in other disciplines, the advantages of this approach to the building design process are further discussed.

Keywords: Sdg7, Affordable And Clean Energy, Sdg11, Sustainable Cities And Communities, Daylight Autonomy, Machine Learning, Building Energy Performance, Synthetic Data-Sets

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