TY - GEN
T1 - A Stochastic Occupancy Modeling Approach to Enhance the Energy Efficiency of Residential Heating and Cooling through Occupancy Sensing Technology
AU - Rakha, Tarek
AU - Sherif, Tarek
AU - Velipasalar, Senem
N1 - Funding Information:
or change set points and set backs significantly. Occupancy schedules in BPS play a key role in representing human presence in buildings and its corresponding building energy consumption. Critical developments in sensing technologies over the last decade make such schedules a candidate for simulation to aid in testing and calibrating human detection systems in buildings. However, such emerging technologies may only have disruptive effects in residential building markets, specifically, if the system is significantly low in cost, easily self-commissioned, and requires minimal maintenance. These stringent constraints make them susceptible to inaccuracies. Therefore, this paper developed a stochastic approach to examine the role of low-cost sensing configurations, like scanning frequency and seasonal performance, in an integrated way for a symbiotic relationship between sensing systems and simulation schedules. The claim, however, remains to be supported by real-life observational data of average false-positive frequencies for different sensing technologies. The mutually beneficial relationship between occupancy schedules and sensing technologies also needs to be utilized, where sensing technology can calibrate occupancy schedules in buildings. Gathered data would constantly shape the ever-changing nature of human behavior, and match occupancy schedules that govern building systems to actual human patterns. Acknowledgements The information, data, or work presented herein was funded in part by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0000940. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. References Annex 66 Final Report. 2018. International Energy Agency, EBC Annex 66. Benezeth, Y., H. Laurent, B. Emile, and C. Rosenberger.
Publisher Copyright:
© International Building Performance Simulation Association, 2022
PY - 2022
Y1 - 2022
N2 - Occupancy schedules are used in Building Performance Simulation (BPS) to act as proxies for human presence. However, they were not previously used to explore the potentials and limitations of human sensing systems. In this paper, we develop a simulation-based approach to support advances in occupancy sensing to specifically examine the impact of sensing errors, such as false positives, of human presence detection systems, by using occupancy schedules to quantify residential building heating and cooling energy use. The aim is to examine varying effects of human detection system configurations on thermal energy consumption in false sensing scenarios, and to introduce occupancy schedules as a means to inform processes of such sensing systems. To extrapolate stochastic transition matrices and generate reliable probabilistically driven occupancy schedules, a Markov-Chain analysis of the 2018 American Time Use Survey (ATUS) is used to develop presence schedules. We then evaluate the impact of false positives in binary occupancy modelling scenarios using Honeybee as a front-end interface in Rhino/Grasshopper, and EnergyPlus as a backend engine. Overall, the aim of this work is to recommend guidelines for various system configurations in which the use of low-cost sensing is justified for heating and cooling regulation.
AB - Occupancy schedules are used in Building Performance Simulation (BPS) to act as proxies for human presence. However, they were not previously used to explore the potentials and limitations of human sensing systems. In this paper, we develop a simulation-based approach to support advances in occupancy sensing to specifically examine the impact of sensing errors, such as false positives, of human presence detection systems, by using occupancy schedules to quantify residential building heating and cooling energy use. The aim is to examine varying effects of human detection system configurations on thermal energy consumption in false sensing scenarios, and to introduce occupancy schedules as a means to inform processes of such sensing systems. To extrapolate stochastic transition matrices and generate reliable probabilistically driven occupancy schedules, a Markov-Chain analysis of the 2018 American Time Use Survey (ATUS) is used to develop presence schedules. We then evaluate the impact of false positives in binary occupancy modelling scenarios using Honeybee as a front-end interface in Rhino/Grasshopper, and EnergyPlus as a backend engine. Overall, the aim of this work is to recommend guidelines for various system configurations in which the use of low-cost sensing is justified for heating and cooling regulation.
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U2 - 10.26868/25222708.2021.30529
DO - 10.26868/25222708.2021.30529
M3 - Conference contribution
AN - SCOPUS:85151495998
T3 - Building Simulation Conference Proceedings
SP - 3552
EP - 3559
BT - BS 2021 - Proceedings of Building Simulation 2021
A2 - Saelens, Dirk
A2 - Laverge, Jelle
A2 - Boydens, Wim
A2 - Helsen, Lieve
PB - International Building Performance Simulation Association
T2 - 17th IBPSA Conference on Building Simulation, BS 2021
Y2 - 1 September 2021 through 3 September 2021
ER -