Understanding Occupancy Patterns in a Commercial Space

Liu, Kin Sum; Munir, Sirajum; Lin, Shan; Shelton, Charles

Title: Understanding Occupancy Patterns in a Commercial Space

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Abstract

Heating, ventilation, and air conditioning (HVAC) is a prime source of energy consumption in the US. In 2006, approximately 35% of energy in the US was used for HVAC [1]. Usually building operators use a static schedule for controlling HVAC systems without having a deeper understanding of how many people use the building at different times of the day. In addition, HVAC systems operate by assuming maximum occupancy in each room, which leads to a significant energy waste, e.g., an HVAC system providing ventilation for 30 people when there are only 10 people in a room. Considering how many people use different rooms at different times of the day is very crucial for achieving building energy efficiency and providing occupant comfort. There have been several attempts to collect long term occupancy patterns from office buildings, e.g., Mitsubishi’s Electronic Research Lab (MERL) dataset [14] and Colorado School of Mines (CSMBB) dataset. However, PIR motion detectors are used in these projects for sensing occupancy and hence these datasets only reflect whether a room is occupied or not without revealing the actual person count. Similar datasets are also collected from households. Only a few datasets from commercial spaces capture actual person count, andmore »« less

Authors:

Liu, Kin Sum ^[1]; Munir, Sirajum ^[2]; Lin, Shan ^[1]; Shelton, Charles ^[2]

Stony Brook Univ., NY (United States)
Robert Bosch LLC, Farmington Hills, MI (United States)

Publication Date:: 2018-02-01

Research Org.:: Stony Brook Univ., NY (United States)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office; National Science Foundation (NSF)

OSTI Identifier:: 1599176

Grant/Contract Number:: EE0007682

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Technical Committee on Cyber-Physical Systems (TC-CPS) Newsletter

Additional Journal Information:: Journal Name: IEEE Technical Committee on Cyber-Physical Systems (TC-CPS) Newsletter; Journal Volume: 3; Journal Issue: 1; Journal ID: ISSN 9999-0038

Publisher:: IEEE CPS

Country of Publication:: United States

Language:: English

Subject:: 42 ENGINEERING; Occupancy pattern; saving energy

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                    Liu, Kin Sum, Munir, Sirajum, Lin, Shan, and Shelton, Charles. Understanding Occupancy Patterns in a Commercial Space.  United States: N. p., 2018. 
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                    Liu, Kin Sum, Munir, Sirajum, Lin, Shan, & Shelton, Charles. Understanding Occupancy Patterns in a Commercial Space.  United States.

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                    Liu, Kin Sum, Munir, Sirajum, Lin, Shan, and Shelton, Charles. Thu .  
"Understanding Occupancy Patterns in a Commercial Space".  United States.  https://www.osti.gov/servlets/purl/1599176.

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@article{osti_1599176,

  title        = {Understanding Occupancy Patterns in a Commercial Space},

  author       = {Liu, Kin Sum and Munir, Sirajum and Lin, Shan and Shelton, Charles},

  abstractNote = {Heating, ventilation, and air conditioning (HVAC) is a prime source of energy consumption in the US. In 2006, approximately 35% of energy in the US was used for HVAC [1]. Usually building operators use a static schedule for controlling HVAC systems without having a deeper understanding of how many people use the building at different times of the day. In addition, HVAC systems operate by assuming maximum occupancy in each room, which leads to a significant energy waste, e.g., an HVAC system providing ventilation for 30 people when there are only 10 people in a room. Considering how many people use different rooms at different times of the day is very crucial for achieving building energy efficiency and providing occupant comfort. There have been several attempts to collect long term occupancy patterns from office buildings, e.g., Mitsubishi’s Electronic Research Lab (MERL) dataset [14] and Colorado School of Mines (CSMBB) dataset. However, PIR motion detectors are used in these projects for sensing occupancy and hence these datasets only reflect whether a room is occupied or not without revealing the actual person count. Similar datasets are also collected from households. Only a few datasets from commercial spaces capture actual person count, and even in these cases, the data collection period was very limited, e.g., in [3] only 5 days of occupancy data is collected as ground truth. We are the first to collect long term (9 months) occupancy data (people count) from an 11,000 square foot commercial office (second floor of Bosch Research and Technology Center Pittsburgh). In this paper, we outline some statistical results from our empirical study and explore their implications for saving energy.},

  doi          = {},

  journal      = {IEEE Technical Committee on Cyber-Physical Systems (TC-CPS) Newsletter},

  number       = 1,

  volume       = 3,

  place        = {United States},

  year         = {2018},

  month        = {2}

}

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