Simon Breslav, Rhys Goldstein, Ben Doherty, Dan Rumery, Azam Khan
Simon Breslav, Rhys Goldstein, Ben Doherty, Dan Rumery, Azam Khan
Symposium on Simulation for Architecture and Urban Design
2013
Accurate building occupancy information can be beneficial in minimizing energy use by improving the intelligence of a Building Automation System (BAS) and helping designers predict the effect of different design options on occupant behavior. However, current occupancy measurements are quite inaccurate due to limitations in sensing technology and the resulting discrepancies between sensor data and what actually happens. In this paper we explore the use of simulation to model occupant behavior in combination with motion sensors to be able to study the relationship between known and measured occupant behavior. An extensible occupancy model, influenced by computational cognitive science and implemented using established modeling conventions is presented along with a simple experiment comparing the effects of different sensor density levels.
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While traditional programming practices have produced a wide range of relatively independent simulation methods, predictive models of extremely complex natural and artificial systems will require a more scalable, more collaborative approach to modeling. This project strives for software that will help researchers develop, debug, document, share, and integrate simulation code.
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.