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

\bibitem{asee_peer_33156}
  Rai, S. (2019, June), \emph{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

Sanish Rai.  "Parallelization of Sequential Monte Carlo Methods in Building Occupancy Simulation and Data Assimilation".  2019 ASEE Annual Conference & Exposition , Tampa, Florida, 2019, June.  ASEE Conferences, 2019.  https://peer.asee.org/33156 Internet.  28 Apr, 2025

\bibitem{asee_peer_33156}
  Sanish Rai.
  "Parallelization of Sequential Monte Carlo Methods in Building Occupancy Simulation and Data Assimilation".
  \emph{2019 ASEE Annual Conference \& Exposition , Tampa, Florida, 2019, June}.
  ASEE Conferences, 2019.
  https://peer.asee.org/33156 Internet.  28 Apr, 2025

@INPROCEEDINGS{asee_peer_33156
author = "Sanish Rai"
title = "Parallelization of Sequential Monte Carlo Methods in Building Occupancy Simulation and Data Assimilation"
booktitle = "2019 ASEE Annual Conference \& Exposition "
year = "2019"
month = "June"
address = "Tampa, Florida"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/33156}
number = {10.18260/1-2--33156}
}

TY  - CPAPER
AB  - 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.
AU  - Sanish Rai
CY  - Tampa, Florida
DA  - 2019/06/15
PB  - ASEE Conferences
TI  - Parallelization of Sequential Monte Carlo Methods in Building Occupancy Simulation and Data Assimilation
UR  - https://peer.asee.org/33156
DO  - 10.18260/1-2--33156
ER  -