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Title: Understanding Occupancy Patterns in a Commercial Space

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 » 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.« less

Authors:
 [1];  [2];  [1];  [2]
  1. Stony Brook Univ., NY (United States)
  2. Robert Bosch LLC, Farmington Hills, MI (United States)
Publication Date:
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

Citation Formats

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