The Impact of Occupancy Behavior
Patterns On the Energy Consumption in
Low-income Residential Buildings
Bing Dong1, Yifei Duan1, Rui Liu2, Taeg Nishimoto2
1
Building Performance and Diagnostics Group, Mechanical Engineering, the
University of Texas, San Antonio, TX, USA
2 College of Architecture, the University of Texas, San Antonio, TX, USA
ME 4343 HVAC Design
Introduction
• Large gaps between measured performance and simulated results
Source: NBI report 2008 Energy Performance of LEED For New Construction Buildings
Introduction
• Occupancy behavior (OB) has significant influence on
building energy use
Introduction
• People spend most of time at homes
100%
unemployed
employed
90 %
80 %
probability
70 %
60 %
50 %
40 %
30 %
20 %
10 %
0 %
12AM
3AM
6AM
9AM
12PM
3PM
6PM
9PM
Based on American time user survey data (ATUS)
12AM
Introduction
• Occupancy behavior is a key factor influencing building energy
consumption and indoor environment
Climate
Condition
Building
Energy
Consumption
Building Envelope
Building Systems
Occupancy
Presence
Occupancy
Behavior
Occupancy
Activities
Occupancy
Operation
UTSA Occupancy Test-beds
• “Three+1” project for Westside low income houses
• A collaborative project of UTSA the San Antonio Alternative Housing
Corporation, and the Texas Department of Housing and Community Affairs
• Honorable Mention for Research and Education in Residential
Construction, presented by City of San Antonio Green Building Awards,
2013
Introduction
AAC House
1,019sf
Container House
1,106sf
SIPs House
1,073sf
Stick House
1,000sf
Instrumentation
Nonintrusive Sensor Network
Temperature Sensor
Powerhouse Dynamics e-Monitor
Energy Consumption
Total Monthly Energy Consumption
12.00
April Energy (kWh/m2)
10.00
8.00
6.00
4.00
2.00
0.00
SIP
# of
Occupants
at homes
2
Stick
Conventional
4
AAC
4
Container
DOE
Benchmark
Model
2
3
Behavior 1: Thermostat Schedule
85
Temperature(F)
80
Stick
AAC
Container
SIP
DOE
Benchmark
75
70
65
60
MON
TUE
WED
THU
FRI
SAT
August 12 to August 19, 2013
All four houses thermostat schedule
SUN
Behavior 1: Thermostat Schedule
HVAC working status for 1 week
SIP house
AAC house
0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00 11.00 12.00 13.00 14.00 15.00 16.00 17.00 18.00 19.00 20.00 21.00 22.00 23.00 24.00
0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00 11.00 12.00 13.00 14.00 15.00 16.00 17.00 18.00 19.00 20.00 21.00 22.00 23.00 24.00
Mon
Mon
Tue
Tue
Wed
Wed
Thur
Thur
Fri
Fri
Sat
Sat
Sun
Sun
On
Off
Behavior 1: Thermostat Schedule
Energy Consumption of HVAC for 1 week(12/8-19/8)
800
sip
stick
Power(W)
Energy Consumption (kWh)
700
600
500
400
300
200
100
0
MON
TUE
WED
THU
FRI
SAT
SUN
Behavior 2: Usage of Major Appliances
Cooling
and
Heating
45%
Energy Consumption of Stick House for 5 months
Building Energy Data Book (2009)
Behavior 2: Usage of Major Appliances (Water Heater)
Energy Consumption of Water Heater for 1 week(12/8-19/8)
Energy Consumption
Power(W) (kWh)
2500
sip
stick
2000
1500
1000
500
0
MON
TUE
WED
THU
FRI
SAT
SUN
Behavior 2: Usage of Major Appliances (Water Heater)
Frequency of Water Heater Usage
25
23
20
16
17
15
10
5
0
SIP
SIP
Stick
Stick
ATUS
ATUS
Behavior 3: Occupancy Movement
Occupancy movement in SIP house
86
master bedroom
Living
Room
kitchen
85
84
Temperature(F)
83
82
81
80
79
78
77
76
MON
TUE
WED
THU
FRI
SAT
SUN
Temperature Profiles of living
room and master bedroom of
SIP house
Behavior 3: Occupancy Movement
100%
High
Probability
90 %
80 %
Probability
70 %
60 %
50 %
40 %
30 %
20 %
10 %
0 %
12AM
3AM
6AM
9AM
12PM
3PM
6PM
Living Room in SIP house
(aggregated one week data)
9PM
12AM
Behavior 3: Occupancy Movement
100%
90 %
80 %
Probability
70 %
60 %
50 %
40 %
30 %
20 %
10 %
0 %
12AM
3AM
6AM
9AM
12PM
3PM
6PM
Kitchen in SIP house
(aggregated one week data)
9PM
12AM
Integrate with Energy Models
2500
sip
stick
Power(W)
2000
1500
1000
500
0
MON
TUE
WED
THU
FRI
SAT
SUN
Appliances
100%
Energy Saving: 15%
Comfort time
Increase: 25%
90 %
80 %
Probability
70 %
60 %
50 %
40 %
30 %
20 %
10 %
0 %
12AM
3AM
6AM
9AM
12PM
3PM
6PM
9PM
12AM
Occupancy Movement
Patterns
New Thermostat
Schedule
Building Controls Virtual Test bed (LBNL)
Measured Energy and
Temperature Data
Conclusion and Future Work
• In this study, we present occupancy behavior and
energy usage patterns in four low income houses
• We also demonstrate possible energy savings based
on occupancy movement
• In future studies, we will:
– Develop statistical models to describe occupancy
behavior in buildings.
– Integrate with energy consumption patterns
IEA Annex 66
• IEA Annex 66 “Definition and Simulation of Occupant Behavior
in Buildings”. UTSA BPD group is task leader of subtask 1.
23 countries and regions
Acknowledgement