Palestra do professor Richard de Dear, apresentada na sede do IAB-RJ, na quinta 27 de fevereiro de 2015.

1. New thermal comfort and air
conditioning behavioral research
methods for residential settings
Prof. Richard de Dear
Dr. Christhina Cândido
Thomas Parkinson
Indoor Environmental Quality Laboratory
Faculty of Architecture, Design and Planning, The University of Sydney

2. History of thermal comfort research
• Over 100 years of thermal comfort research
• Overwhelming majority of that research focused on
office environments, followed by
– health care environments,
– schools,
– vehicular cabins,
– outdoor and semi-outdoors etc.
• It’s leap of faith that one set of research findings
from one type of environment can be generalized
across diverse settings – deterministic logic

3. What’s missing?
• These investigations have made great contributions
to our understanding of thermal comfort in last 100
years
• However, what has been largely missing from the
discourse is an understanding of comfort inside
residences
• Yet we spend most of our lives inside our homes?

4. What’s so different about homes?
• Why would we expect residential comfort to be different?
– Greater adaptive opportunities
– Different and more flexible clothing patterns
– Energy price signal directly affects the comfort consumer
– Different occupant activities indoors
• These contextual factors must influence thermal perceptual
processes, yet very little work has been done in this
context, because there’s no requirement for temperature
standardisation
• However, there are significant policy drivers relating to the
the energy expended in the pursuit of comfort in the
residential sector

5. Policy drivers
• Greenhouse emissions
– In Australia over the last 20 years there has been 40%
growth in direct CO2 emissions from the residential
sector
• Peak electricity demand
– Air-conditioning now ranks as one of the fastest
growing end-uses of electricity in Australian
residential sector
– In New South Wales (incl. Sydney) more than 60% of
households now own at least one air-conditioning
system

6. Australian Home Insulation Program
• “The Energy Efficient Homes Package,” which included the “Home
Insulation Program” introduced in 2011
• Policy driven by technocrats who ignored the human comfort and
behavioral dimensions of heating and cooling related GHG emissions in
the residential sector
• Again, this was based on deterministic comfort logic:
– IF comfort stays constant AND housing envelope efficiency increases THEN
electricity demand (incl. peak demand) and greenhouse gas emissions
decrease
• But the householders behaved quite differently
– IF housing envelope efficiency increases AND electricity demand (and GHG
emissions) remain static or even increase THEN comfort can increase
– This is the behavioral economist’s concept of “rebound effect”

7. The Rebound Effect
• Defined as “The behavioral or other systemic responses to the
introduction of new technologies that increase the efficiency
of resource use. These behavioral responses tend to offset the
beneficial effects of the new technology.
• As a result of the rebound effect, actual electricity of GHG
savings are significantly less than savings calculated by
technocrats using deterministic comfort logic

8. Why is residential comfort
so under-researched?
• It’s clear that there are many good reasons for
understanding thermal comfort in the residential
sector better than we do at present, so why are
residential comfort studies so rare?
• Logistics!
– In offices, you get permission to obtain objective and
subjective comfort evaluations within a concentrated
sample (i.e. it’s quick and easy!)
– People’s homes are geographically dispersed, scheduling
and logistics are difficult, issues with long-term installation
of equipment, and the ongoing ethics concern of
householder privacy

9. Previous residential studies
• Hunt & Gidman (1982) found their data to be
biased from a cold spell during measurement
period – temporal sampling issues
• The “Warm Front” initiative following the
European heat wave had only initial surveys to
support half-hourly measurements that lasted 2-
4 weeks in each house
• Lomas & Kane (2012) reported having only 49%
useable data from 312 households after all their
missing or incomplete observations were
removed

10. Our new Smart[phone] Approach
• Phenomenal growth in smartphone penetration over
the past few years (in Australia now 84% and rising)
• Interactive touch devices offer excellent user
experience
• Always-on and tethered to their owners
• Generates data with high spatial and temporal
resolution
• Footloose respondents don’t have to be seated at their
computer desk to complete a questionnaire
Smartphones present a direct, frictionless
link between researcher and questionnaire respondent

11. Introducing ‘Comfort Chimp’
smartphone questionnaire
• Questionnaires created through Online Control Panel
• Participants receive a personalised SMS invitation containing a
hyperlink to the questionnaire
• Questionnaires are issued to participants by an SMS gateway
through their local telecom provider (<US$0.04 for each SMS
questionnaire in most countries)
• Each response is coded to a respondent and time-stamped
• Responses can be viewed by researchers in real time via the Online
Control Panel

13. Comfort Chimp
• For our longitudinal research design the comfort
questionnaires were designed to be very quick
(<1 minute to complete)
• Sent to householders at times most relevant to the research
questions e.g.
– When subjects are home using air conditioning
– During heatwaves
– During peak electricity demand episodes
– During cold snaps etc
• For our study on heatwaves the frequency of questionnaire
sends varied from 1/week to 3/day, depending on weather
• Duration of our longitudinal study was 18 months

14. Temperature and RH measurements
• Maxim ‘iButtons’
• Cheap (US$25), self-sufficient,
hermetically sealed devices
• ±0.5°C accuracy
• 0.5°C resolution (8-bit)
• 3 months storage at 15-min
sampling interval
• 10 year battery life
• Temperature + relative
humidity version also
available (US$40)

15. Determining AC usage patterns
• iButtons were placed within the occupied zone of
different rooms of each house
• An iButton was also installed in the supply air vent of
the AC terminal unit
I’m here!

16. Defining AC usage
• An Excel macro used to determine ‘AC events’
– If dT/dt of AC supply air >|X| then AC was just
switched ON
– If ΔT between the AC and occupied zone
temperatures is > +X then system is in heating
mode;
– if < –X then system is in cooling mode
– X is empirically determined for each household

17. Householder Questionnaire
• Contextual information about the house and its
occupants obtained at time of iButton installation
• Includes descriptions of…
– building physics,
– householder demographics,
– householder socio-economics,
– HVAC equipment (type, installed rooms, capacity, age etc).

18. Sydney results using this method…
• 220 years of temperature history inside 45 homes
• 7.65 million data points
• 2,100 at-home questionnaire responses
• 4,900 AC events (“switch-on”)
• 11,800 hours of AC use

19. Residential Clothing
Insulation Behaviour
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0.2clo 0.4clo 0.6clo 1clo

20. Residential AC events by month
0
100
200
300
400
500
600
700
800
900
NumberofACEvents
Cooling Heating

21. AC duration
0
200
400
600
800
1000
1200
NumberofACEvents
Cooling Heating

22. Time of AC use
Cooling Heating
00:00 - 06:00
06:00 - 10:00
10:00 - 16:00
16:00 - 00:00

23. AC ‘trigger’ temperatures
0%
5%
10%
15%
20%
25%
30%
35%
40%
8-10 10-12 12-14 14-16 16-18 18-20 20-22 22-24 24-26 26-28 28-30 30-32 32-34 34-36 36-38
Cooling Heating

24. Adaptive Comfort
5
10
15
20
25
30
35
40
5 10 15 20 25 30
IndoorACTriggerTemperature
Running Mean Outdoor Temperature
Adaptive Model Compliance Cooling Heating
Above 80% 23% 0%
Below 80% 1% 64%
Within Range 77% 36%

25. Cooling Trigger Temps
0%
5%
10%
15%
20%
25%
30%
35%
40%
20-22 22-24 24-26 26-28 28-30 30-32 32-34
Daytime Night

26. Heating Trigger Temps
0%
5%
10%
15%
20%
25%
30%
35%
8-10 10-12 12-14 14-16 16-18 18-20 20-22 22-24 24-26
Morning Night

27. Bedroom Temps
10
12
14
16
18
20
22
24
26
28
30
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
OccupiedZoneTemperature(degC)
Average Bedroom Temperatures at Night (10PM-7AM)
With AC Without AC

28. Living Room
10
12
14
16
18
20
22
24
26
28
30
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
OccupiedZoneTemperature(degC)
Average Livingroom Temperatures during the day (7AM-10PM)
With AC Without AC

29. Average Thermal Sensation
Living Rooms
0,0
0,5
1,0
1,5
2,0
23-24 24-25 25-26 26-27 27-28 28-29
Indoor Air Temperature (degC)
Without AC With AC
Physics Physiology Psychology Behaviour
Warm
Slightly
Warm
Neutral
73 19 102 26 94 35 67 22 59 19 31 12

30. Average Thermal Sensation
Living Rooms
0,0
0,5
1,0
1,5
2,0
23-24 24-25 25-26 26-27 27-28 28-29
Indoor Air Temperature (degC)
Without AC With AC
Warm
Slightly
Warm
Neutral
73 19 102 26 94 35 67 22 59 19 31 12
Psychology Behaviour

31. Conclusions
• Context matters!
• Comfort is not deterministic
(physics > physiology > psychology > behaviour)
• Residential AC usage patterns are not 100% rational
• Implications for peak electricity demand;
Time-of-use tariffs, but what about frivolous-use tariffs?
• Australia is an economy with cheap energy, rapidly rising AC
penetration in residential sector, and excessively high GHG
emissions per capita
• What about the emerging BRICs?
– This is a well-developed, turn-key research method so let’s
collaborate!

32. Thank you for your attention