Exploring Information and Comfort Expectations Related to the Use of a Personal Ceiling Fan
Abstract
:1. Introduction
Research Gap and Scientific Contribution
- To what extent do occupants’ different expectations of the indoor environment and adaptive possibilities influence their a) thermal and indoor air quality perception and b) their satisfaction with a type of PECS?
- To what extent can tailored information to activate normative motivations be used to manipulate thermal and indoor air quality perception and satisfaction with a type of PECS of occupants with different expectations?
2. Literature Review, Definitions and Hypotheses
2.1. Thermal and Behavioral Expectations
- Thermal expectations: the thermal experience foreseen by occupants; the anticipated result, their perception of what will occur.
- Behavioral expectations: the likelihood of engaging in a specific behavior to adapt to the thermal environment to improve their comfort.
2.2. Provided Information and Building Interactions
2.3. Normative Motivations
2.4. Hypotheses
- H1: A person with more positive expectations about the thermal conditions in the room and towards a type of PECS will find the climatic conditions more acceptable, expressing higher thermal satisfaction than a person with more negative expectations.
- H2: By activating normative motivations through tailored information, expectations can be influenced in a positive direction so that (a) participants with more positive expectations will express higher thermal satisfaction and (b) participants with more negative expectations will show a change in expectations after using the PECS.
3. Methods
3.1. Recruitment and Participation
3.2. Pre-Test: Online Questionnaire
3.3. Session in the LOBSTER
3.3.1. Classification of Expectancy Groups
- Using a training dataset, the cluster structure was calculated to explain a selected threshold of 80% of the variance using the k-means method [56].
- As the k-means method requires the number of clusters as an input, the elbow method was applied to calculate the optimal number of clusters.
- A test dataset was fitted to the obtained cluster structure using a support vector machine (SVM) method [57], which is a class of supervised learning algorithms that train the classifier function using labeled data.
3.3.2. Manipulation Technique
3.4. Data Analysis
3.4.1. Sample Size and Checks on Random Assignment
3.4.2. Hypotheses Testing: Statistical Tests
4. Results
5. Discussion
5.1. Practical Implications
5.2. Limitations
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
BMI | Body mass index |
BMS | Building management system |
EQ | End questionnaire |
IEQ | Indoor environmental quality |
Int | Intercept |
IQ | Initial questionnaire |
LOBSTER | Laboratory for Occupant Behavior, Satisfaction, Thermal comfort, and Environmental Research |
OQ | Online questionnaire |
PCA | Principal component analysis |
PECS | Personal environmental control system |
SD | Standard deviation |
SQ | Start questionnaire |
SVM | Support vector machine |
WHO | World Health Organization |
Appendix A. Additional Tables
Cluster 1 | Cluster 2 | Cluster 3 | Full Sample | Test of Independence | |||
---|---|---|---|---|---|---|---|
N | N | N | N | χ2 | df | p-Value | |
Sex | 0.15 | 2 | 0.928 | ||||
Female | 13 | 13 | 9 | 35 | |||
Female | 14 | 17 | 10 | 41 | |||
Age | 0.15 | 2 | 0.929 | ||||
Young | 18 | 19 | 13 | 50 | |||
Elderly | 9 | 11 | 6 | 26 | |||
BMI | 6.12 * | 2 | 0.047 | ||||
Normal | 12 | 20 | 15 | 47 | |||
Overweight | 15 | 10 | 4 | 29 | |||
Daytime | 0.47 | 2 | 0.079 | ||||
Morning | 12 | 16 | 9 | 37 | |||
Afternoon | 15 | 14 | 10 | 39 | |||
Office | 4.67 | 2 | 0.097 | ||||
1 | 9 | 18 | 11 | 38 | |||
2 | 18 | 12 | 8 | 38 | |||
Video | 0.52 | 2 | 0.771 | ||||
Short | 15 | 14 | 9 | 38 | |||
Long | 12 | 16 | 10 | 38 | |||
Experience with fans | 5.13 | 2 | 0.077 | ||||
Yes | 2 | 4 | 6 | 12 | |||
No | 25 | 26 | 13 | 64 | |||
Experience with ceiling fans | 2.05 | 2 | 0.359 | ||||
Yes | 7 | 8 | 2 | 17 | |||
No | 29 | 22 | 17 | 59 | |||
Previous worked in office | 7.25 * | 2 | 0.027 | ||||
Yes | 9 | 3 | 8 | 20 | |||
No | 18 | 27 | 11 | 56 |
Cluster 1 | Cluster 2 | Cluster 3 | Test of Independence | |||
---|---|---|---|---|---|---|
M (SD) | M (SD) | M (SD) | χ2 | df | p-Value | |
Actual mood a | 3.04 (1.02) | 2.57 (1.14) | 3.21 (1.13) | 4.53 | 2 | 0.104 |
Air velocity level [%] | 55.48 (21.44) | 46.52 (29.62) | 48.92 (25.10) | 2.29 | 2 | 0.318 |
Duration fan on [min] | 127.99 (5.69) | 123.35 (19.50) | 124.00 (24.87) | 2.90 | 2 | 0.235 |
Video rating 1 b | 0.21 (0.89) | −0.21 (1.14) | 0.04 (1.23) | 3.28 | 2 | 0.194 |
Video rating 2 b | 0.03 (0.99) | −0.04 (1.02) | 0.02 (1.04) | 0.12 | 2 | 0.940 |
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Measure | Description of Item | Response Categories | Mean (SD) |
---|---|---|---|
Thermal sensation a | “Wie fühlen Sie sich jetzt gerade?” (How do you feel right now?) | −3 (cold) to +3 (hot) | 4.79 (0.55) |
Thermal comfort a | “Empfinden Sie dies als…” (Right now, do you find this environment…?) | 1 (extremely uncomfortable) to 5 (comfortable) | 3.87 (0.55) |
Thermal preference a | “Wie hätten Sie es jetzt gerade lieber?” (Right now, would you prefer to be…?) | 1 (much cooler) to 7 (much warmer) | 3.29 (0.54) |
Thermal acceptability a | “Wie empfinden Sie diese Temperaturbedingungen jetzt gerade?” (Right now, do you find the thermal environment…?) | 1 (clearly unacceptable) to 4 (clearly acceptable) | 3.47 (0.55) |
Indoor air quality perception a | “Wie nehmen Sie die Raumluftqualität im Büro wahr?” (How do you perceive the indoor air quality in the office?) | 1 (very good) to 7 (very bad) | 4.26 (1.02) |
Fan satisfaction b | To maintain comfortable indoor temperatures, the ceiling fan is more effective than I expected; To maintain comfortable indoor temperatures, the ceiling fan is more effective than I expected; If I could choose, I would rather use a ceiling fan than open the windows; I have control over the personal ceiling fan; The ceiling fan is easy to operate; The ceiling fan fits well with the floor plan and furnishings of the office; I can understand the advantages of the ceiling fan; The ceiling fan is quiet; Being able to adjust the air velocity myself is an advantage of the ceiling fan; Improving the indoor climate is a benefit of using the ceiling fan; If I could choose, I would use the fan as an energy-saving cooling strategy; If I could choose, I would use a ceiling fan instead of turning on an air conditioner; I consider myself capable of operating the personal ceiling fan; I should avoid opening the window when it is very warm outside. | 1 (strongly disagree) to 7 (strongly agree) | 8.24 (0.76) [6.05, 9.40] d |
Fan expectations c | Same as before, but slightly modified and adapted in the form of “I expect that …” | 1 (strongly disagree) to 7 (strongly agree) | 4.53 (2.11) [1.52, 8.83] d |
Estimate | Std. Error | z-Value | p-Value | |
---|---|---|---|---|
Cluster 2 a | 1.65 | 0.67 | 2.45 | 0.015 * |
Cluster 3 a | 0.076 | 0.66 | 0.11 | 0.909 |
BMI (overweight) | −0.42 | 0.56 | −0.76 | 0.449 |
Experience (yes) | −0.91 | 0.60 | −1.50 | 0.133 |
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Rissetto, R.; Schweiker, M. Exploring Information and Comfort Expectations Related to the Use of a Personal Ceiling Fan. Buildings 2024, 14, 262. https://doi.org/10.3390/buildings14010262
Rissetto R, Schweiker M. Exploring Information and Comfort Expectations Related to the Use of a Personal Ceiling Fan. Buildings. 2024; 14(1):262. https://doi.org/10.3390/buildings14010262
Chicago/Turabian StyleRissetto, Romina, and Marcel Schweiker. 2024. "Exploring Information and Comfort Expectations Related to the Use of a Personal Ceiling Fan" Buildings 14, no. 1: 262. https://doi.org/10.3390/buildings14010262