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Implementation of occupant behaviour models into EnergyPlus Simulation Software

Burak Gunay

Liam O’Brien

Ian Beausoleil-Morrison

Outline

• Motivation for occupant behaviour modelling

• A brief-review of the modelling methodologies

• Implementation of occupant models in BPS

• A tutorial on using occupant models in BPS

• Design example (operable vs. fixed windows)

• Operation example (manual vs. automated lighting controls)

• Concluding remarks

• Unresolved issues and future work

2

Occupant behaviour modelling: problem and solution • Problem: occupant behaviour

has become the leading

unknown of building

performance as building

envelopes and

mechanical/electrical

systems become more

efficient.

• Reported ranges in energy

use:

• Residential: as high as a factor of 20

• Commercial: as high as a factor of 3

3

12 Ottawa House Load Comparison

Non-HVAC: Appliances, lighting, hot water

(Saldanha, Beausoleil-Morrison, 2012)

Factor

of 4

# of occupants is a

reasonable good predictor;

but hard to predict during

design

4

Background

5 identical Danish houses; Source: Gram-Hanssen (2010)

• Family #3 thought they

were very energy-

conscious 5

“Seduced by the View”

(Urban Green Council, USGBC, 2013)

Their conclusion: we should be critical of highly-glazed facades 6

Design implications of Occupants

• Engineers are risk-adverse and usually assume worst-

case conditions for equipment sizing (lights on, fully-

occupied, blinds up, windows’ gas-fill leaked, etc.)

• This leads to significant oversizing -> capital cost and inefficient operating conditions

• For net-zero energy buildings, occupant uncertainty

pushes renewable energy systems to be bigger

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%7

Design implications of Occupants

• More important than equipment sizing, neglecting occupant behaviour models can lead to sub-optimal design

• Example: daylight availability alone might tell designers to maximize window area. But accounting for adaptive response (shading), more modest windows can out-perform large ones for daylight.

8

Potential opportunities from occupants

• Diverse behaviours can smoothen peak loads

• Occupants are not powerless sensors of

environmental conditions, but active participants

• We can loosen controls if occupants are in control

9

Design for comfort and occupant behaviour

But no future opportunities for adjustment; so get it right!

Occupant

Behaviour

Smart Controls

Fixed/Passive Design 10


But extreme care must be taken to not irritate occupants 11


But disaggregate as much as possible

Occupant

Behaviour

Smart Controls

Fixed/Passive Design 12

Major research questions

• How transferable/universal are occupant behaviour

models?

• 9 contextual factors were established in a recent literature review

• Is there a risk of optimizing to the wrong occupant

behaviour models?

• What’s more valuable: low mean predicted energy or

low uncertainty?

• How should performance uncertainty be

represented? And will designers buy in? Will they

have confidence in OB models?

• How do occupants respond to buildings that have

been designed/optimized using OB models? 13

A brief-review of the modelling methodologies • There are many independent research groups

studying occupant behaviours and presence all

over the world.

• They monitor a group of occupants’ behaviours

and presence for sometime and…

•A review of the modelling methodologies •Implementation of occupant models in BPS •A tutorial to use occupant models in BPS •Concluding remarks •Unresolved issues and future work

14

A brief-review of the modelling methodologies • …identify the factors influencing these behaviours

(e.g. turning on the lights) and their reversals (e.g.

turning off the lights)…

•A review of the modelling methodologies •Implementation of occupant models in BPS •A tutorial to use occupant models in BPS •Concluding remarks •Unresolved issues and future work

15

A brief-review of the modelling methodologies • …often try to come up with mathematical relations

(a.k.a. occupant models) between a set of

predictors to explain an occupants’ behaviour.

11

1

exp( )

( ) or ( )

1 exp( )

n

Markov Chains Bernouilli k k

kt t t n

k k

k

a b x

P S S P S

a b x

• Regression coefficients for a

and b can be pictured as the

human traits defining our

tendency to undertake a

behaviour.

•A review of the modelling methodologies •Implementation of occupant models in BPS •A tutorial on using occupant models in BPS •Concluding remarks •Unresolved issues and future work

16

A brief-review of the modelling methodologies • A major challenge is to integrate these occupant

behaviour models in BPS:

• Barriers to this:

• prerequisite knowledge of stochastic processes (e.g., discrete-time vs. discrete-event Markov Chains or Bernoulli random processes or survival models)

• lack of user-friendly interfaces to develop custom controls in BPS tools (e.g. BCVTB)

• prior knowledge of imperative programming

• often BPS users (in industry and even in research) are not fully convinced that occupant models are essential


17

Implementation of occupant models in BPS

• To this end:

• 18 occupant models for occupants’ lighting, window, and interior shading use, clothing adjustment, and occupancy were incorporated in EMS application of EnergyPlus.

• Two tutorials (a design and an operation example) are provided to demonstrate how occupant models can help industry make informed design and operation choices.


18

19

20

21

A tutorial on using occupant models in BPS:

Operable windows vs. Fixed windows in Ottawa (Annual Res.)


http://www.nrcan.gc.ca/energy/product

s/reference/14700 22

A tutorial on using occupant models in BPS: Operable windows vs. Fixed windows in Ottawa (Monthly Res)


• Note the variations in

energy use during the

shoulder seasons.

• Occupants do not use

their windows too often

in the peak heating

season.

23

A tutorial on using occupant models in BPS: Operable windows vs. Fixed windows in Ottawa (Monthly Res)


• Energy use in summer

months is higher due to

cooling.

• The uncertainty and

magnitude of the energy use

decreased substantially.

24

A tutorial on using occupant models in BPS: Operable windows vs. Fixed windows in Ottawa (Focus on a day in April)


25

A tutorial on using occupant models in BPS: Lighting Operation in Perimeter Office Spaces in Ottawa


• Consider a scenario in which the facilities

management department is about to determine the

operations mode of lighting controls (by simply

changing the position of the DIP switches) for the

perimeter offices:

1. Manual on and Manual off

2. Automation on&off

3. Automation off and manual onsf

26

A tutorial on using occupant models in BPS: Lighting Operation in Perimeter Office Spaces in Ottawa


1. Manual on and Manual off

• Acts as simple occupant control light switches

2. Automation on & off

• Automatically turns on the lights only when it is

less than 500 lux on the workplane and the

occupant is present —turn off otherwise.

3. Automation off and manual on

• Automatically turns off the lights when it is

more than 500 lux on the workplane or the

occupant is absent.

• Occupants are responsible to turn on.

27

A tutorial to use of occupant models in BPS: Lighting Operation in Perimeter Office Spaces in Ottawa

•A review of the modelling methodologies •Implementation of occupant models in BPS •A tutorial to use occupant models in BPS •Unresolved issues and future work

28

A tutorial on using of occupant models in BPS: Lighting Operation in Perimeter Office Spaces in Ottawa

• With manual control, there is substantial

individual variability.

• There is clearly no reason to turn on the

lights automatically in an office.

• Things to be corrected are:

• when an occupant does not realize the daylight potential.

• when an occupant leaves the lights on upon departure.

29

30

Concluding remarks

• State-of-the-art occupant models for lighting,

windows, window shades, clothing level

adjustments, and presence have been implemented

in BPS.

• A tutorial is included to demonstrate how better

design and operation decisions can be achieved

using BPS with occupant models.

• Scalable efforts like this may also initiate a long-term

shift in common perception that questions the

practicality of occupant models.


31

Unresolved issues and future work

• The existing occupant models arise from different

observational studies. In many cases, the contextual

factors during these observational studies were not

properly reported.

• Occupant models are simulated (and calibrated) with

discrete-time formalism. This forces users to select a

fixed and prescribed time-step (i.e. 5 min). Discrete-

event based simulation algorithms will be

investigated.

• All of these models as implemented in EnergyPlus

and the tutorial examples will be made publicly

available.


32

IEA EBC Annex 66: Definition and Simulation of Occupant Behaviour • 90 researchers from a dozen countries

• Main scope: occupancy, residential, offices

• Major objectives:

• Establish monitoring protocol

• Establish modelling protocol

• Integrate OB models into prominent simulation engines

• Demonstrate applicability of OB modelling/simulation

• Annex66.org

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Thank you! Any questions?

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Date post:	30-May-2017
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