Best-practices Guideline on Advanced Occupant Modeling · Advanced occupant modeling What are...
Transcript of Best-practices Guideline on Advanced Occupant Modeling · Advanced occupant modeling What are...
Best-practices Guideline onAdvanced Occupant Modeling
Sara GilaniPost-doctoral Fellow
Liam O’BrienAssociate Professor
Content
• Part 1: Basic theories
• Part 2: Implementation of advanced occupant models in EnergyPlus
• Part 3: Sensitivity analysis
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Part 1:
Basic theories
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Why do occupants matter in buildings?
BuildingOperating conditions
Occupant(s)
Performance
• Occupants are not passive recipients of buildings.
• They actively and continually attempt to improve comfort.
Why do occupants matter in buildings?5
(Saldanha, Beausoleil-Morrison, 2012)
factor of 4
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Lights 10 W/m2
Blinds 10% transmittanceSouth-facing office in Ottawa
(Burak Gunay)
Why do occupants matter in buildings?
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Energy Use
Pro
bab
ility Renewable energy generation
capacity for 50% chance of achieving net-zero energy
90% chance of achieving net-zero energy
Uncertainty from occupant behaviour
Occupant effects: 10 to 1000%
Why do occupants matter in buildings?
BehaviorsTriggers
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Comfort:
• Thermal
• Visual
• Acoustic
• IAQ
Adaptive:
• Turning on lights
• Opening/closing windows/blinds
• Adjusting thermostats
• Changing clothing level
Non-adaptive:
• Occupancy (i.e. presence of occupants)
• Using plug-in equipment
• Turning off lights
Contextual factors:
• Control systems
• Cost of energy
• Social constraints
What are occupant behaviors?
What are occupant behaviors?9
Indirectly affects
heating/cooling
load
Directly affects
energy and affects
heating/cooling load
Affects heating/
cooling load
Occupancy
(presence)
Window shade/
blind use
Operable window
use
Office plug-in
equipment use
Thermostat use
Personal fan use
Personal heaters
use
Clothing level
Metabolic rate
Lighting use
Heating
Cooling
Lighting
Office equipment
Legend
Direct physical causal effect
Indirect physical causal effect
Comfort related causal effect
(O’Brien et al., 2018)
Key occupant behaviors and how they may impact building energy performance:
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Two main occupant modeling approaches:• Standard-based models
What are occupant modeling approaches?
Lig
ht sc
hedul
e
Time of day (hours)
Static deterministic model
Uncertainty bounds for static stochastic model
Pro
bab
ility
of lig
ht
bein
g turn
ed o
n
Illuminance
Dynamic deterministic model
Dynamic stochastic model
0 24
1
0
Lig
ht schedule
Time of day (hours)
Static deterministic model
Uncertainty bounds for static stochastic model
Pro
babili
ty o
f lig
ht
bein
g turn
ed o
n
Illuminance
Dynamic deterministic model
Dynamic stochastic model
0 24
1
0
• Advanced occupant models
(O’Brien et al., 2018)
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What are occupant modeling approaches?
• Standard-based models are the current practices in modeling occupant in buildings.
• Limitations:• Inaccurate prediction of building performance• Poor design decision-making based on the inaccurate
simulation results
(O’Brien et al., 2018)
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(O’Brien, Gunay, 2015)
BuildingOperating conditions
Occupant(s)
Performance
Comfort conditions
Adaptive actions
Advanced occupant modeling
What are advanced models?
1. Dynamic
2. Stochastic
3. Agent-based
4. Data-driven
Pro
ba
bili
ty
Building performance(O’Brien et al., 2018)
What are advanced model forms?13
1. Markov chain:This model form predicts whether an occupanttakes an action in the next timestep or next event:1. Discrete-time2. Discrete-event
2. Bernoulli:This model from predicts the state of a buildingsystem or component.
3. SurvivalThis model form predicts the duration of a stateright before an event happens.
Which occupant models to use?
Occupant modeling strategies vary for different applications:
• What is our aim from simulating a building or a room-level model?
• How big is the building we simulate?
• What type of building we simulate?
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What is our aim from simulating a building or a room-level model?
• Code compliance
• HVAC design
• Net-zero energy design
• Comfort assessment
• Façade design
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How big is the building we simulate?
• The larger the building size, the lower the uncertainty in the predicted whole-building energy use
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(Gilani et al., 2018)
What type of building we simulate?• Energy uses of various building types have different
sensitivities to occupant behaviors.
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Regular staff/
Employees
Building owner/
manager
Visitors/guests/customers/
patients/students
Motel/hotel
Nursing home/care
facility
Retail
School
Medical office
Food/beverage store
Hospital
Non-med. office
Warehouse
(O’Brien et al., 2018)
Use cases:
• Whole-building energy prediction
• Building system and plant equipment sizing
• Net-zero energy buildings
• Occupant comfort
• Façade design
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Which occupant models to use?
Whole-building energy prediction
• Current method: Standard schedules
• Proposed method:Small buildings:
Agent-based models
Medium to large buildings:Static-deterministic models for non-adaptive behaviors and dynamic-deterministic models for adaptive behaviors
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(Gilani et al., 2018)
Building system and plant equipment sizing20
• Current method: Standard schedules
Lack of potential to evaluate the risk of downsized building system and plant equipment
• Proposed method:Zone-level:
Static-deterministic (i.e. standard schedules)
Building level:Static-stochastic models
heatingheating (std. sched.)
cooling
cooling (std. sched.)
(O’Brien et al., 2018)
Net-zero energy buildings21
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0.58 0.66 0.74 0.82 0.9 0.98 1.06 1.14 1.22
Cu
mu
lati
ve
pro
bab
ilit
y
Annual electricity use GWh
90
% N
ZE
95
% N
ZE
99
% N
ZE
99
.9%
NZ
E
Standard
schedules
ASHRAE
Std. 90.1
Stochastic schedules
99.9
% N
ZE
99%
NZ
E
95%
NZ
E90%
NZ
E
Sta
nd
ard
AS
HR
AE
sched
ule
s
(Abdelalim, O’Brien, 2018)
• Current method: Standard schedules
Lack of accurate predicted building energy use
• Proposed method:static-stochastic or dynamic-stochastic models
Provide an insight into the impact of occupant-related uncertainties on building energy use
Occupant comfort22
• Current method: Standard schedules
Lack of distribution of energy use and number of occupants’ interactions with buildings
• Proposed method:Dynamic-deterministic or dynamic-stochastic models
Potential for robust design by reducing occupants’ interactions with buildings
(O’Brien, Gunay, 2015)
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(Gilani et al., 2016)
Façade design
• Current method: Standard schedules
Lack of providing an insight into occupant-related uncertainty and occupants' interactions with facades' components
• Proposed method:Dynamic-deterministic or dynamic-stochastic models
Potential for optimal design
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Part 2:
Implementation of advanced models in EnergyPlus
Discrete-time Markov chain models
𝑝 𝑠𝑤𝑖𝑡𝑐ℎ 𝑜𝑛 𝑜𝑓𝑓 =𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑡𝑖𝑚𝑒𝑠𝑡𝑒𝑝𝑠 𝑤𝑖𝑡ℎ 𝑎 𝑙𝑖𝑔ℎ𝑡 𝑠𝑤𝑖𝑡𝑐ℎ 𝑜𝑛
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑜𝑐𝑐𝑢𝑝𝑖𝑒𝑑 𝑡𝑖𝑚𝑒𝑠𝑡𝑝𝑒 𝑤ℎ𝑒𝑛 𝑙𝑖𝑔ℎ𝑡𝑠 𝑤𝑒𝑟𝑒 𝑜𝑓𝑓
𝑝 =𝑒𝛽0
+σ𝑖=1𝑛 𝛽𝑖𝑥𝑖
1 + 𝑒𝛽0+σ𝑖=1
𝑛 𝛽𝑖𝑥𝑖
(Burak Gunay)
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Advanced occupant models from literature26
EnergyPlus EMS Application
(Gunay et al. 2015)
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EnergyPlus EMS ApplicationEMS variables • Sensors
• Actuators• Built-in variables• Global variable
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Daylight sensor in the center and at the workplane height (0.8 m)
South-facing shoebox model29
EnergyPlus EMS Application
• We will implement advanced occupant models for:
1. Occupancy: Wang et al.’s (2005) model
2. Light: Reinhart’s (2004) model
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0.0
0.2
0.4
0.6
0.8
1.0
0 0.2 0.4 0.6 0.8 1
Vac
ancy
du
rati
on
Random number
Wang et al.’s occupancy model
Current time Occupancy stateWang et al.’s model
EMS Sensor EMS Program EMS Actuator
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Vacancy duration: Exponential distribution
Event time (arrival, departure, lunch, two breaks): Normal distribution
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
6 7 8 9 10
Pro
bab
ility
Event time
Event time Mean (hr) Std (hr)
Arrival 8
0.25
First break 10Lunch 12
Second break 15Departure 18
Vacancy duration Mean (hr) Std (hr)
First break 0.25 0.1Lunch 1 0.25
Second break 0.25 0.1
0.0
0.2
0.4
0.6
0.8
1.0
0 200 400 600 800 1000
Pro
bab
ility
of
ligh
t sw
itch
-on
Workplane illuminance (lx)
Reinhart’s light switch model
•Current time•Occupancy•Daylight
Light stateReinhart’s model
EMS SensorEMS Program EMS Actuator
𝑝 =𝑒𝛽0
+𝛽1𝑥1
1 + 𝑒𝛽0+𝛽
1𝑥1
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Light switch-on:
x = workplane illuminance
Light switch β0 (μ±σ) β1 (μ±σ)
Switch-onArrival 1.6 ± 0.3 −0.009 ± 0.002
Intermediate -3.9 ± 0.5 −0.002 ± 0.0005Switch-off at departure -1.3 ± 0.3 0.003 ± 0.001
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Part 3:
Sensitivity analysis
Sensitivity analysis
Glazing systems design parameters
Type U-factor (kWh/m2) SHGC VT
1 1.82 0.36 0.642 1.42 0.48 0.69
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• Two window shade control systems:
(1) blinds are closed all the time
(2) blinds are open all the time
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Sensitivity analysis
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(Abuimara et al., 2018)
Sensitivity analysis
• Impact of energy conservation measures (ECMs) on the predicted building energy use
Sensitivity analysis
• Various parameters:
o ECMs
o Climate zones
oBuilding types
oBuilding sizes
oBuilding users-related domains
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ReferencesAbdelalim A, O’Brien W. 2018. An approach towards achieving net-zero energy buildings based on a stochastictenant model. In: eSim 2018. Montreal, Canada.
Abuimara T, O'Brien W, Gunay HB, Carrizo JS. 2018. Assessing the impact of changes in occupants on designdecision making. In: eSim 2018. Montreal, Canada.
Gilani S, O'Brien W, Gunay HB. 2018. Simulating occupants' impact on building energy performance at differentspatial scales. Building and Environment 132:327-337.
Gilani S, O’Brien W, Gunay HB, Carrizo JS. 2016. Use of dynamic occupant behavior models in the buildingdesign and code compliance processes. Energy and Buildings: Special Issue on Advances in BEM and Sim117:260-271.
Gunay HB, O'Brien W, Beausoleil-Morrison I. 2016. Implementation and comparison of existing occupantbehavior models in EnergyPlus" Journal of Building Performance Simulation.
O'Brien W, Abdelalim A, Abuimara T, Beausoleil-Morrison I, Carrizo JS, Danks R, Gilani S, Gunay HB, Kesik T, OufM. 2018. Roadmap for occupant modelling in building codes and standards. In: eSim 2018. Montreal, Canada.
O’Brien W, Abdelalim A, Gunay HB. 2018. Development of an office tenant electricity use model and itsapplication for right-sizing HVAC equipment. Journal of Building Performance Simulation.
O’Brien W, Gunay HB. 2015. Mitigating office performance uncertainty of occupant use of window blinds andlighting using robust design. Building Simulation:1-16.
Reinhart CF. 2004. Lightswitch-2002: a model for manual and automated control of electric lighting and blinds.Solar Energy 77:15-28.
Saldanha N, Beausoleil-Morrison I. 2012. Measured end-use electric load profiles for 12 Canadian houses athigh temporal resolution. Energy and Buildings 49:519-530.
Wang D, Federspiel CC, Rubinstein F. 2005. Modeling occupancy in single person offices. Energy and Buildings37:121-126.
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Tareq AbuimaraPhD studentCivil Engineering
Aly Abdelalim, PhDPost-docEnvironmental Engineering
Sara Gilani, PhDPost-docCivil Engineering
Mohamed Ouf, PhDPost-docCivil Engineering
Prof. Liam O’Brien Prof. Burak Gunay Prof. Ian Beausoleil-Morrison
Carleton project team39
Partners and Sponsors40