6332 Day-Lighting Buildings

Christoph ReinhartEnergy

Occupant BehaviorRules of Thumb

Energy Considerations

GSD 6332 – Occupant Behavior

Occupant Behavior Energy Considerations

Daylight Factor x Design Sky Daylight Autonomy

Course OutlineWeek 1: Course Introduction

Shading StudiesWeek 2: Climate Files

Daylighting Rules of ThumbWeek 3: Simulation I (Ecotect) Simulation II (Radiance)Week 4: Simulation III (more Radiance) Visit MIT – LAM & Partners Week 5: Simulation IV (Daysim) Occupant Behavior

Week 6: El. Lighting & Fixture Design Scale ModelsWeek 7: Midterm Project Critique Lighting Simulation IVWeek 8: Instructor away (no class) Field Trip - Visit KalwallWeek 9: Lighting Controls Toplighting (VELUX)Week 10: Case Studies (ARUP) Commissioning (LBNL)Week 11: Complex Fenestration Systems Thanksgiving (no class)Week 12: Aesthetics of light (Sampson) Art in SimulationWeek 13: Project Progress Review Light and HealthTBD: Final Project Critique

Oct 27 - Midterm Project CritiqueMisc

You will learn about …

occupant use of personal controls

Objective of today’s lecture

Review - Dynamic Daylight Simulations As opposed to a static simulation that only considers

one sky condition at a time, dynamic daylight simulations generate annual time series of interior illuminances and/or luminances.

Demo: Ecotect Export to Daysim- run static simulation- simulation parameters

- *ill files

Daylight Factor x Design Sky versus Daylight Autonomy

Daylight Factor x Design Sky Daylight Autonomy

Museum Lighting

Annual Light Exposure: established upper threshold for artwork – already established used used for museums (CIE TC3-22 ‘Museum lighting and protection against radiation damage’)

category material classification

example of materials

lighting illuminance

limiting annual exposure

I insensitive metal, stone, glass, ceramic

no limit no limit

II low sensitivity canvases, frescos, wood, leather

200 lux 600 000 lux h /yr

III medium sensitivity

watercolor, pastel, various paper

50 lux 150 000 lux h/yr

IV high sensitivity

silk, newspaper, sensitive pigments

50 lux 15 000 lux h/yr

Museum Lighting RequirementsCIE TC3-22 ‘Museum lighting and protection against radiation damage’

Example: Seattle Art Museum - Arup Lighting using Daysim 3D model of site and building

ARUP Lighting

source: http://irc.nrc-cnrc.gc.ca/ie/light/RadianceWorkshop2005/PDF/Franks_ArupCaseStudies.pdf

Example: Seattle Art Museum - Arup Lighting using DaysimSidelit Gallery ARUP Lighting

Example: Seattle Art Museum - Arup Lighting using Daysim

Museum Open Hours - 1,500,000+ lux-hours ARUP Lighting

Example: Seattle Art Museum - Arup Lighting using DaysimAutomatic Shading + Switching - 555,000 lh ARUP Lighting

Wrigley Global Innovation Center Chicago, Illinois – AEC

simulation: AEC

simulation: AEC

• Winter Garden Atrium break area

• Views from adjacent offices

• Illuminance hours requirements for ficus trees

simulation: AEC

Wrigley Global Innovation Center Chicago, Illinois - AEC

simulation: AEC

Occupant Behavior

0

20

40

60

80

100

0.5 1 1.5 2 2.5 3.3 4.5 5.5 6.5 7.5distance to facade [m]

dayl

ight

aut

onom

y [%

]

blinds always fully closed

blinds always down, slats at 45 o

blinds always up

USER BEHAVIOUR ?!

architecture: Meier-Weinbrenner-Single, Nürtingen

• passive house standard• advanced glazing• SHW, PV

• ventilation heat recovery• ground heat exchanger • night ventilation

Reinhart, Voss 2003Monitoring User Behavior

HOBO data logger

IlluminanceTemperature

occupancy

Monitoring Setup in the Offices

receiver2414.5 MHz

data acquisitionEIB systemBlind setting

video surveillance camera

Monitoring Blind Usage

0

0.25

0.5

0.75

1

0 100 200 300 400 500 600minimum work plane illuminance [lux]

switc

h-on

pro

babi

lity

at a

rriv

al

0

0.25

0.5

0.75

1

0 100 200 300 400 500 600minimum work plane illuminance [lux]

switc

h-on

pro

babi

lity

at a

rriv

al

type 1

type 2

Jim Love, University of Calgary

Switch-On Probability (I)

0

0.25

0.5

0.75

1

0 100 200 300 400 500 600minimum work plane illuminance [lux]

switc

h-on

pro

babi

lity

at a

rriv

al

Hunt 1978Lamparter 2000

Switch-On Probability (II)

0

0.2

0.4

0.6

0.8

1

0 100 200 300 400 500minimum work plane illuminance [lux]

switc

h-on

pro

babi

lity

at a

rriv

al

People are Consistent but Different

0

0.25

0.5

0.75

1

switc

h-of

f pro

babi

lity

whe

n le

avin

g

<30 minutes30-59 minutes

1-2 hours2-4 hours

4-12 hours12-24 hours

24+ hours

Pigg et al. University of Wisconsin

0

0.25

0.5

0.75

1

switc

h-of

f pro

babi

lity

whe

n le

avin

g no controls

<30 minutes30-59 minutes

1-2 hours2-4 hours

4-12 hours12-24 hours

24+ hours0

0.25

0.5

0.75

1

switc

h-of

f pro

babi

lity

whe

n le

avin

g no controls

occupancy sensor

<30 minutes30-59 minutes

1-2 hours2-4 hours

4-12 hours12-24 hours

24+ hours

behavioral patterns change in the presence of automated controls

0

0.25

0.5

0.75

1

switc

h-of

f pro

babi

lity

whe

n le

avin

g no controlsdimmed systemoccupancy sensor

<30 minutes30-59 minutes

1-2 hours2-4 hours

4-12 hours12-24 hours

24+ hours

Switch-Off Probability

Manual blind control model

Daysim: active (energy conscious) or passive user

Associate work plan sensor with window

Note: this step requires to couple individual sensors together.

Benefit: Direct comparison between daylighting concepts with and without movable and/or fixed shading devices

work plane sensors

window with blinds

) )

) ) )

) ) )

)

) ) )

annual occupancyprofiles

annual illuminanceprofiles

Lightswitch 2002

Lightswitch Algorithm(stochastic)

el. lighting/blinds profile

Reinhart, 2002

Model Overview

Switch lights on

Is work place already occupied?

YES

stochastic process:switch on probability

(Fig. 7-5)

NO

NO

YES

NOYES

Has the room been deserted for longer than sensor

delay time?

Are lights switched on?

Does occupant arrive?

Is there an occupancy sensor?

YES

YES

Switch lights off

NO YES

Does occupant leave?

Are lights switched on?NO

YES

stochastic process:Pigg’s switch off probability with or without occupancy

sensor (Fig. 7-5)

NONO

NO

Switch lights off

YES

YES

Does occupant leave?

NO

YES

stochastic process:intermediate switch on

probability (Fig. 7-7)

NO YES

Switch lights on

Does occupant arrive?YES

NO

NO0

0.2

0.4

0.6

0.8

1

0 100 200 300 400 500 600minimum work plane illumiance [lux]

switc

h-on

pro

babi

lity

HuntLamparter

stochastic process:switch on probability

Manual Lighting Control Algorithm

field study approach

Example I – single office

Located in Ottawa Canada

Taken from Daysim tutorial

office building three lighting zones

Example I – single office

same results for North and South offices no daylight on the central aisle

Daylight factor simulation

Example I – single office

ample amount of daylight in both offices up to 30% DA on aisle => on/off switch with timer

Daylight Autonomy simulation

Lighting ControlsPhotocell-controlled Dimming with Occupancy Sensor

Occupancy sensor.

Photocell.

Demo: Daysim- active and passive behavior

Example I – single office

absolute comparison of different control strategies reference case is manual on/off switch with venetian blinds

Electric Lighting Use in South facing Office

0

0.5

1

1.5

2

2.5

3

manual on/off switch switch-off occupancysensor

on/off occupancysensor

dimming system dimming system withswitch-off occupancy

sensor

dimming system withon/off occupancy

sensor

annu

al e

lect

ric li

ghtin

g us

e [k

Wh/

ft2 yr]

reference case

By Oct 23rd: Work through the rest of the Getting Started document Read through Daylighting Metrics Paper Read Daysim tutorial pages 18 -32 Voluntarily: Read Rendering with Radiance Chapters 10 - 13

Reading:

Questions…