• GitHub
  • Home
  • People
  • Research
  • Publications
  • Courses
  • News
  • Contact
  • Internal
UT Shield

Intelligent Environments Laboratory

The University of Texas at Austin
  • Home
  • People
    • Prof. Zoltan Nagy, PhD
    • June Young Park
    • José Ramón Vázquez-Canteli
    • Megan K. McHugh, MSE
    • Ayşegül Demir Dilsiz
    • Hagen Fritz
  • Research
  • Publications
  • GitHub
  • Courses
  • News
  • Contact

April 15, 2022, Filed Under: News

New Paper: Occupancy detection from WiFi without ground-truth

We have a new paper out in Building & Environment, led by our former lab member Prof June Park (UT Arlington).

June Young Park, Kingsley Nweye, Edward Mbata, Zoltan Nagy, CROOD: Estimating crude building occupancy from mobile device connections without ground-truth calibration, Building and Environment, Volume 216, 2022,
https://doi.org/10.1016/j.buildenv.2022.109040.

Abstract: The occupancy information in buildings is fundamental for smart buildings (e.g., occupant-centric controls). Opportunistic occupancy detection (OOD) uses connection data of mobile devices. While OOD has been developed and applied, one critical drawback is that it requires the ground truth of occupants to calibrate, which is limited to gather. Here, we introduce CROOD: a capture and recapture (CRc) inspired OOD. In ecology, CRc has been established for the estimation of animal populations, when the manual count is impossible. We adopt this unique approach to estimate the number of mobile devices in a building. Then, using a simple estimate on the total population, CROOD determines the relationship between the numbers of occupants and mobile devices. We evaluate CROOD numerically on the synthetic building populations and demonstrate its application in a university library using WiFi connection data. We find that CROOD can estimate the number of mobile devices and subsequently the number of occupants with 1–2 weeks to converge a reasonable accuracy. A long term experiment shows that CROOD can adapt to varying population characteristics (e.g., occupants bring more mobile devices), outperforming the reference sample mean estimator. The real building implementation demonstrates that while in the first 1–2 weeks, the sample mean estimator is superior, eventually CROOD adapts and provides better estimates without ground-truth calibration. Although CROOD has a limitation of building types and systems, our results envision that CROOD could be a viable addition to other OOD methods to better utilize existing mobile device connection data to estimate occupancy in buildings.
Keywords: Smart building; Occupant behavior; Building occupancy; Opportunistic sensing; Capture and recapture estimation

Research Highlight

Fault detection and diagnostics of air handling units using machine learning and expert rule-sets

To reduce HVAC energy inefficiencies, fault detection and diagnostics (FDD) has become a growing field of interest. In particular, air handling units Read more 

About Us

The Intelligent Environments Laboratory (IEL), led by Prof. Zoltán Nagy, is an interdisciplinary research group within the Building Energy & Environments (BEE) and Sustainable Systems (SuS) Programs of the Department of Civil, Architectural and Environmental Engineering (CAEE) in the Cockrell School of Engineering of the University of Texas at Austin.

The aim of our research is to rethink the built environment and define Smart Buildings and Cities as spaces that adapt to their occupants and reduce their energy consumption.

We combine data science with building science and apply machine learning to the building and urban scale

Take a look at our projects !

Tags

air handling unit Annex 79 architecture artificial neural network Bluetooth city learn Community engaged research earthquakes environmental monitoring fault detection and diagnostics HVAC integrated design intelligent energy management Lighting Control machine learning Megan McHugh multi-agent systems Occupancy Occupant Centered Control Reinforcement Learning Review Smart Building smart city teaching Thermal Comfort
Tweets by Z0ltanNagy

Research

  • All Projects

UT Energy App – Privacy Policy

Fault detection and diagnostics of air handling units using machine learning and expert rule-sets

Reinforcement Learning in the Built Environment

Reinforcement learning for urban energy systems & demand response

Multi-Agent Reinforcement Learning for demand response & building coordination

IEA-EBC Annex 79: Occupant Centric Design and Operation of Buildings

People

  • Prof. Zoltan Nagy, PhD
  • June Young Park
  • José Ramón Vázquez-Canteli
  • Megan K. McHugh, MSE

Tags

air handling unit Annex 79 architecture artificial neural network Bluetooth city learn Community engaged research earthquakes environmental monitoring fault detection and diagnostics HVAC integrated design intelligent energy management Lighting Control machine learning Megan McHugh multi-agent systems Occupancy Occupant Centered Control Reinforcement Learning Review Smart Building smart city teaching Thermal Comfort
ITS

301 E Dean Keeton St
Austin, TX 78712
512-555-5555
nagy@utexas.edu

UT Home | Emergency Information | Site Policies | Web Accessibility | Web Privacy | Adobe Reader

© The University of Texas at Austin 2022

  • All Projects