Thermal Comfort-Based Personalized Models with Non-Intrusive Sensing Technique in Office Buildings
Abstract
:Featured Application
Abstract
1. Introduction
1.1. Background of Thermal Models for HVAC System of Office Buildings
1.2. Background of Sensing Technique for Developing Thermal Models in Office Buildings
1.3. Research Contributions
2. Methodology
2.1. Experimental Setup
2.2. Development of Thermal Models
2.3. Assessment of Thermal Models
3. Result Analysis
3.1. Relationships between Occupant-related Variables and Thermal Sensations
3.2. Results of Thermal Models with Classification Algorithms
4. Discussions
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Variable | Sensor Tool | Resolution | Operating Range |
---|---|---|---|
Skin temperature (°C) | FLIR B8400 | 320 × 240 pixels | −20 °C~120 °C |
Clothing surface temperature (°C) | FLIR B8400 | 320 × 240 pixels | −20 °C~120 °C |
Indoor air temperature (°C) | DHT22 | 0.1 °C | −4 °C–80 °C |
Indoor relative humidity (%) | DHT22 | 0.1 °C | −40 °C–80 °C |
Clothing insulation (clo) | Manually identify the clothing type and find the insulation values based on ASHRAE 55 |
Thermal Sensation Vote (TSV) | Description |
---|---|
−3 | very cold |
−2 | cold |
−1 | cool |
0 | neutral |
1 | warm |
2 | hot |
3 | very hot |
Date | Start Time | Transition Time | End Time | Start Temperature (°C) | Transitional Temperature (°C) | End Temperature (°C) |
---|---|---|---|---|---|---|
3/11 | 9:50 | 11:00 | 11:50 | 17.4 | 29.3 | 22.3 |
13:45 | 15:20 | 16:45 | 22.6 | 29 | 24.3 | |
19:30 | 21:45 | 22:30 | 19.3 | 28.5 | 20.9 | |
3/12 | 10:10 | 11:10 | 11:30 | 17.7 | 29.5 | 25.3 |
13:40 | 14:55 | 15:30 | 21.2 | 28.4 | 24.4 | |
3/13 | 9:35 | 11:20 | 12:00 | 19 | 28.9 | 24.1 |
13:40 | 17:30 | 29.9 | 18.5 | |||
19:40 | 21:05 | 22:35 | 21.2 | 28.5 | 24.3 | |
3/14 | 9:30 | 10:50 | 11:35 | 20.8 | 29 | 26.5 |
13:35 | 16:50 | 17:35 | 17.3 | 29.3 | 23.8 | |
20:00 | 20:45 | 22:30 | 20.9 | 28 | 18.7 | |
3/15 | 9:35 | 11:05 | 11:40 | 19.5 | 29.5 | 24 |
13:30 | 14:50 | 16:30 | 21.5 | 30 | 18.1 | |
3/16 | 8:50 | 9:00 | 10:20 | 16.1 | 14.4 | 26.2 |
Algorithms | Parameters | Values |
---|---|---|
SVM | Penalty parameter C | 1, 10 |
Kernel type | Gaussian, linear | |
RF | Tree depth | 3, 4 |
Number of trees | 50, 100 |
Predicted Negative | Predicted Positive | |
---|---|---|
Actual Negative | True negative | False positive |
Actual Positive | False negative | True positive |
Very Cold (−3) | Cold (−2) | Cool (−1) | Neutral (0) | Warm (1) | Hot (2) | Very Hot (3) | |
---|---|---|---|---|---|---|---|
Female | 3 | 16 | 29 | 168 | 73 | 58 | 15 |
Male | 7 | 13 | 24 | 188 | 100 | 58 | 23 |
Model Configuration | Average Recall Score | |||||
---|---|---|---|---|---|---|
Female | Male | |||||
Base | A | B | Base | A | B | |
SVM, C = 1, kernel = linear | 0.65 | 0.98 | 0.65 | 0.62 | 0.96 | 0.57 |
SVM, C = 1, kernel = Gaussian | 0.62 | 0.68 | 0.57 | 0.61 | 0.78 | 0.59 |
SVM, C = 10, kernel = linear | 0.62 | 0.98 | 0.6 | 0.6 | 0.96 | 0.6 |
SVM, C = 10, kernel = Gaussian | 0.62 | 0.82 | 0.52 | 0.6 | 0.88 | 0.58 |
RF, Max_depth = 3, number of trees = 50 | 0.6 | 0.96 | 0.6 | 0.68 | 0.95 | 0.59 |
RF, Max_depth = 3, number of trees = 100 | 0.6 | 0.96 | 0.59 | 0.58 | 0.96 | 0.61 |
RF, Max_depth = 4, number of trees = 50 | 0.63 | 0.97 | 0.59 | 0.57 | 0.99 | 0.62 |
RF, Max_depth = 4, number of trees = 100 | 0.62 | 0.99 | 0.59 | 0.6 | 0.98 | 0.6 |
Algorithm | Base Feature Set | Feature Set A | Feature Set B | ||||||
---|---|---|---|---|---|---|---|---|---|
P (%) | R (%) | F1 (%) | P (%) | R (%) | F1 (%) | P (%) | R (%) | F1 (%) | |
SVM | 62.3 | 62.9 | 62 | 100 | 100 | 100 | 69.2 | 64.7 | 65.1 |
RF | 57.7 | 48.6 | 48.6 | 91.6 | 91.4 | 91 | 49 | 47 | 46.3 |
PMV | 48.6 | 48.6 | 48.3 | 48.6 | 48.6 | 48.3 | 48.6 | 48.6 | 48.3 |
Algorithm | Base Feature Set | Feature Set A | Feature Set B | ||||||
---|---|---|---|---|---|---|---|---|---|
P (%) | R (%) | F1 (%) | P (%) | R (%) | F1 (%) | P (%) | R (%) | F1 (%) | |
SVM | 33.5 | 42.5 | 35.8 | 97.5 | 95 | 96.1 | 32.1 | 43.6 | 34.5 |
RF | 28.8 | 40 | 33.1 | 90.4 | 92.5 | 91.4 | 26.8 | 38.5 | 30.7 |
PMV | 35 | 33 | 31.8 | 35 | 33 | 31.8 | 35 | 33 | 31.8 |
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Lu, S.; Wang, W.; Wang, S.; Cochran Hameen, E. Thermal Comfort-Based Personalized Models with Non-Intrusive Sensing Technique in Office Buildings. Appl. Sci. 2019, 9, 1768. https://doi.org/10.3390/app9091768
Lu S, Wang W, Wang S, Cochran Hameen E. Thermal Comfort-Based Personalized Models with Non-Intrusive Sensing Technique in Office Buildings. Applied Sciences. 2019; 9(9):1768. https://doi.org/10.3390/app9091768
Chicago/Turabian StyleLu, Siliang, Weilong Wang, Shihan Wang, and Erica Cochran Hameen. 2019. "Thermal Comfort-Based Personalized Models with Non-Intrusive Sensing Technique in Office Buildings" Applied Sciences 9, no. 9: 1768. https://doi.org/10.3390/app9091768