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Parallelization of Sequential Monte Carlo Methods in Building Occupancy Simulation and Data Assimilation

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2019 ASEE Annual Conference & Exposition


Tampa, Florida

Publication Date

June 15, 2019

Start Date

June 15, 2019

End Date

October 19, 2019

Conference Session

Computing Research II

Tagged Division

Computing and Information Technology

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Paper Authors


Sanish Rai West Virginia University Institute of Technology

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SANISH RAI is an Assistant Professor in the Department of Computer Science and Information Systems at West Virginia University Institute of Technology, Beckley, WV. He received his Ph.D. degree from Georgia State University in 2017. His research interests include simulation and modeling, agent and graph based systems, artificial intelligence, data assimilation and machine learning. His email address is

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Building occupancy models for large building and occupancy is a computationally costly simulation. Data can be collected about the building from various sensors installed in the building and can be used for real-time estimation modelling. The simulation model and real-time estimation algorithm both are complex models requiring resource to accommodate for increase in size of the building environment, its occupants and their activities. In our previous work, we presented a graph-based agent-oriented model to efficiently simulate large number of occupants and used Sequential Monte Carlo (SMC) methods to assimilate the occupancy data for real-time estimation. A main issue with SMC algorithm is its high computational cost, which increases proportionally with the simulation model size. To manage the need of resource when increasing particles, we implement parallelization to SMC and improve the runtime of overall building occupancy simulation and estimation.

Rai, S. (2019, June), Parallelization of Sequential Monte Carlo Methods in Building Occupancy Simulation and Data Assimilation Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--33156

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