Occupant behavior simulation and definition in buildings
Transcript of Occupant behavior simulation and definition in buildings
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Annex 66 Definition and Simulation of
Occupant Behavior in Buildings
Da Yan
Tsinghua University, China
Mar 12, 2015
Energy Efficiency and Behavior Workshop
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Background
• Large gaps between field data and simulation result
Source: NBI report 2008
Energy Performance of LEED
For New Construction Buildings
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Background
• OB has significant influence on building energy use
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住户编号
空调能耗指标
(kW
h/m
2)
平均值 2.3 kWh/m2 Average 2.3kWh/m2
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sum
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(kW
h/m
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Apartment No.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
住户编号
空调能耗指标
(kW
h/m
2)
平均值 2.3 kWh/m2 Average 2.3kWh/m2
En
erg
y C
on
sum
pti
on
of
Co
olin
g S
yste
m
(kW
h/m
2)
Apartment No.
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ctri
city
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nsu
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tio
n o
f C
oo
ling
Sys
tem
0123456789
101112131415
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
住户编号
空调能耗指标
(kW
h/m
2)
平均值 2.3 kWh/m2 Average 2.3kWh/m2
En
erg
y C
on
sum
pti
on
of
Co
olin
g S
yste
m
(kW
h/m
2)
Apartment No.
0123456789
101112131415
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
住户编号
空调能耗指标
(kW
h/m
2)
平均值 2.3 kWh/m2 Average 2.3kWh/m2
En
erg
y C
on
sum
pti
on
of
Co
olin
g S
yste
m
(kW
h/m
2)
Apartment No.
Ele
ctri
city
Co
nsu
mp
tio
n o
f C
oo
ling
Sys
tem
The statistics energy consumption of cooling system in different apartments of one residential building in Beijing,2006
Significant discrepancy between each apartment
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Impact of OB on energy consumption
Stefano Corgnati, POLITO
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Impact of OB on EE technology evaluation
What kind of thermal insulation level would be adapted
in Shanghai residential building?
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Impact of OB on EE technology evaluation
OB is a key factor in the evaluation of building technology
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• VRV system consumes less energy in both Beijing and Shanghai area
• But, VRV’s COP is at the same level of central cooling system
Impact of OB on EE technology evaluation
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32.03
16.89
0
5
10
15
20
25
30
35
CWC VRV
kWh
/( m
2·a
)
Measured BEC for cooling in two Buildings, 2010
Impact of OB on EE technology evaluation
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CWC system: use AC system almost all rooms at the same time
Impact of OB on EE technology evaluation
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VRF system: use AC system in a part time part space way
Impact of OB on EE technology evaluation
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Impact of OB on EE technology evaluation
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Background
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Interaction between OB with system
• Employees are encouraged to wear a tie in their office during winter in
Hong Kong, to have lower indoor temperature setting to save energy
• Nevertheless…
• Due to internal heat gains, the office continuously supply cooling during
winter time
• The lower indoor set point will induce to higher energy consumption
• There are quite a lot integration and interaction between building fabric,
occupant behavior and mechanical system
• We need a methodology to quantitatively measure the occupant
behavior’s effect on total energy usage in building
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Importance and Urgency
• OB is a Key factor for design optimization, energy diagnosis and
performance evaluation, and also building energy simulation
• Limited understanding or inadequate over-simplification on OB;
• In-depth quantitative analysis urgently needed;
• Over 20 groups all over the world studying OB individually
• Lack of consensus in common language, in good experimental design, and
in modeling methodologies.
• An international cooperation is extremely important for both knowledge
gaining and data sharing
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Importance and Urgency
Simulated Result
Measured Data ?
OCCUPANT
BEHAVIOR Building Energy Use estimation
Building Technology evaluation
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IEA-EBC-ANNEX66 Definition and Simulation of Occupant Behavior in Buildings
www.ANNEX66.org
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Research Target
• Identify quantitative definition, description and classification of OB
• Develop effective simulation methodologies of OB
• Integrated OB models with building energy simulation tools
• Demonstrate the OB models in design, evaluation, operation management and policy making by case studies
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Research Target
• Quantitative methods & common language for OB description and simulation
• Develop a scientific framework for OB quantitative definition and simulation methodologies
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Participants 24 Countries
Australia Austria Belgium Brazil Canada China
Denmark Finland France Germany Hungary Italy
Japan Korea Netherland Norway Poland Portugal
Spain Sweden Singapore Turkey UK USA
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Participants
• 24 Nations, 69 institutions
• 114 participants, plus 13 participants want to be kept informed
• University, research institute, software company, design consultant company, operation manager, system control company
• ASHRAE has confirmed to join this project, IBPSA, REHVA and CIBSE are considering participation
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Scope
Focus on how OB physically and quantitatively affect on building performance simulation
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Challenges
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Stochastic process
Zhu, 2011/5-6 Wang,2011/7
Turn off
Turn off
8:00
Turn on
Turn on
0:00
ON
OFF
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Stochastic process
0 2 4 6 8 10 12 14 16 18 20 22 24
occupancy s
tate
time of day (office 4)
1-31
1-28
1-27
1-26
1-25
1-24
Personal level Building level
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Diversity
Other responses include: complain, contact facilities department, keep blankets and sweaters within reach, and open windows.
IFMA 2009 HVAC Survey of IFMA members in US and Canada with 452 responses from 3357 samples
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Diversity
• A so called “typical persons” and their distribution are essential to connect between the academic research and policy making
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Complexity
Behavior may be triggered by multiple factors for an individual
And behavior would interactive with each others
Questionnaire survey results in Chengdu
Opening mode
a Never on
b Always on in summer
c On as long as entering
d On feeling hot
e On regular at ___ o’clock
f On when guests come
g Others
AC Operating Modes in Living-room
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Technical approach
Occupant movement and
presence
Action Model in residential buildings
Action Model in commercial
buildings
Integration of OB model with
simulation tools
Demonstration of the applications of
OB models
Sub-Task A Sub-Task B Sub-Task C
Sub-Task D
Sub-Task E
Fundamental Research
Practical Application
Targeting Building types:
Residential buildings & Office buildings
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ST-A Occupant presence and movement model
Occupant’s presence and movement is strongly connected with Space, Time and Events
Occupant Presence & Movement
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ST-A Occupant presence and movement model
Building level – # of occupants
• Q: How many occupants are there in a building at a time?
Space level – occupied status
• Q: whether or not a space (room) is occupied?
Space level – # of occupants
• Q: How many occupants are there in a space at a time?
Occupant level - individual tracking
• Q: In which space an occupant is at a particular time?
A set of coherent occupant presence models are demanded for different application purposes
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Example of Occupant Movement Model
Characteristic parameters for movement Weekday
schedule Event Valid Period Characteristic parameters of occupants
Working time
8:00~17:00
Lunch time
12:00~13:00
Go to office 7:00~8:30 Mean morning arrival time 7:45
Leave for lunch 11:30~12:30 Mean leaving time 12:00
Return after lunch 12:30~13:30 Mean return time 13:00
Get off work 17:00~21:00 Mean night departure time 18:00
Walk around 8:00~17:00
proportion of
time
mean sojourn
time in room
In own office 0.93 3h
In other rooms 0.06 10min
In outside 0.01 10min
Meetings 8:00~17:00 See table for meeting rooms
Close 23:00 Closing time 23:00
Type of meeting
room
Occupied time
proportion
Mean duration per
time
Minimum
attendees Meeting type
Meeting room 0.2 1h 2 Group meeting 2/3
Mixed 1/3
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Demo. of simulation results
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ST-B Action model in residential buildings
Occupant’s actions are influenced by environmental and physical parameters in a stochastic way
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ST-B Action model in residential buildings
Action Model
Object State
OB Characteristic
Parameter
Object State Change
State based Action Based
Action based models has more advantage to exhibit the relationship between OB phenomenon and physical driven force
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ST-C Action model in commercial buildings
0.006
0.095
0.163
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
Single Office Medium Office Large Office
kWh
/m2/d
ay
Lighting energy consumption
Higher possibility of interaction and negotiation among occupants in commercial buildings
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ST-D Integration with simulation software
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ST-D Integration with simulation software
Essential to integrate the OB models with BEMs to exhibit the influence of OB on building energy and performance
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ST-D Integration with simulation software
Develop flexible, sustainable, robust module for simulation
DeST platform
Attributes of the building
Occupancy module
Lighting operation
module
Occupancy and lighting
power
AC & window
operation module
Engine for temperature
and load simulationAC/window
state
Room t
Coupled calculation of AC/window/temperature
Accdb database
SQLite database
Statistical analysis
Statistical analysis
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ST-E Applications of OB models
To exhibit OB’s influence on comfort, environment, energy usage and technology adaptability, improve applications by case studies & guidelines
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Outlook of Occupant Behavior Research
Building Energy
Use
Indices for Behavior
Social or Psychological
survey
Questionnaire SurveyModels
Occupancy & Operation Operation
Mode
Distribution of Typical Occupant
Typical Occupant
Data Collection
Software
Application
Hardware Technology
Formulation of Standards
Policy-makingEvaluation
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Work plan
• Preparation phase
– One year (2013.11 — 2014.11)
• Working phase
– Two and a half years (2014.11 —
2017.6)
• Reporting phase
– Half a year (2017.6 — 2017.12)
2013.11 2014.11 2015.11 2017.6 2017.12
Preparation phase
Working phase
Reporting phase
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Outcomes
Outcomes Target Audience
1
Standard definition, description and
classification of occupant behaviour in
building
Building Energy Researchers
Energy Modellers
Simulation Software Developers
2
Systematic measurement approach,
simulation modelling and validation
methodology
3 Occupant Behavior Database with data of
different temporal and spatial resolution
4 Software to simulate OB, integrated with a
building thermal and energy model
Building Designers
Energy Saving Evaluators
HVAC Engineers
System Operators
Energy Policy Makers 5
Case studies and guidelines to demonstrate
applications of the new OB definitions and
models
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Activities
Aug. 23rd, 2013, Paris, 24 participant
International Workshop for New ANNEX
1st expert meeting in Hong Kong
March 12 to 14, 2014, 39 participants
Seminar at ASHRAE Seattle Conference
About 100 people attended the seminar
2nd expert meeting in Nottingham
August 4th to 6th, 53 participants
Will be held in LBNL on March 30
to April 1, 2015
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Summary
• OB has great influence on building energy usage and also technology
evaluation
• There are still lack of quantitative methods, scientific criteria and
common language for OB description and simulation
• ANNEX 66 is focused on setting up a scientific framework for OB
definition, description, simulation and applications in the coming four
years efforts
• We are looking forward to cooperation and working with the teams all
over the world to devote into Occupant Behavior Simulation research