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Towards Visualization of Simulated Occupants and their Interactions with Buildings at Multiple Time Scales (2:55 min.)
Video title (x:xx min.)
While most building simulation tools model occupancy using simple 24-hour profiles, researchers are applying machine learning and other advanced modeling approaches to simulate individual occupants and their interactions with buildings. For building designers to fully benefit from these increasingly advanced occupant models, visualizations must ultimately reveal subtle yet informative patterns contained in the simulation results. As a step in this direction, we focus on 3D animation and the challenges that arise when multiple time scales are involved. Specifically, we explore the use of stylized computer animation to clarify occupant movement, the use of cueing to draw attention to key events, and an original clock widget to consolidate time-related information.
Buildings are the largest consumers of energy responsible for 48% of all Green House Gas (GHG) emissions. Due to the complexity and multidisciplinary aspects of architectural design, construction, urban design, and building occupant behavior, simulation has gained attention as a means of addressing this enormous challenge. The idea is to model a building’s many interacting subsystems, including its occupants, electrical equipment, and indoor and outdoor climate. With simulation results in hand, an architect is better able to predict the energy demand associated with various designs, and choose from among the more sustainable options.
Visual data representations leverage the power of human perception to process complex information, and through interaction, garner new insights. Our research focuses on visualizing data from a wide variety of domains and fundamentally tackles the question, what makes a visualization effective? We explore novel visual encodings and interaction techniques, multiscale approaches, and even simulation to bridge human and automated analysis of multivariate, time-series, and graph data, ultimately aiding in hypothesis generation, testing, and sense making.
This project investigates the properties and qualities of multiscale datasets in an effort to gain critical insights needed, in user experience and understanding, to make progress in increasingly complex contexts.