A Novel Data-Driven Model for the Effect of Mood State on Thermal Sensation
Abstract
:1. Introduction
2. Materials and Methods
2.1. Design of the Experiments
2.2. Data Collection for Environmental Parameters
2.3. Mathematical Model Derivation
3. Results
3.1. Measurement Results
3.2. Survey Results
3.3. The MSCF Results
3.4. Limitations
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Appendix B
References
- Cucchiella, F.; D’Adamo, I.; Gastaldi, M.; Miliacca, M. Efficiency and allocation of emission allowances and energy consumption over more sustainable European economics. J. Clean. Prod. 2018, 182, 805–817. [Google Scholar] [CrossRef]
- Evin, D.; Acar, A. Energy impact and eco-efficiency of the envelope insulation in residential buildings in Turkey. Appl. Therm. Eng. 2019, 154, 573–584. [Google Scholar] [CrossRef]
- Du, Y.F.; Jiang, L.; Duan, C.; Li, Y.Z.; Smith, J.S. Energy consumption scheduling of HVAC considering weather forecast error through the distributionally robust approach. IEEE Trans. Ind. Inf. 2018, 14, 846–857. [Google Scholar] [CrossRef]
- Lindelof, D.; Afshari, H.; Alisafaee, M.; Biswas, J.; Caban, M.; Mocellin, X.; Viaene, J. Field tests of an adaptive, model–predictive heating controller for residential buildings. Energy Build. 2015, 99, 292–302. [Google Scholar] [CrossRef]
- Hu, C.; Xu, R.; Meng, X. A systemic review to improve the intermittent operation efficiency of air-conditioning and heating system. J. Build. Eng. 2022, 60, 105136. [Google Scholar] [CrossRef]
- Hong, Y.; Yoon, S.; Choi, S. Operational signature-based symbolic hierarchical clustering for building energy, operation, and efficiency towards carbon neutrality. Energy 2023, 265, 126276. [Google Scholar] [CrossRef]
- ASHRAE 55-2020; Thermal Environmental Conditions for Human Occupancy. The American Society of Heating, Refrigerating and Air-Conditioning Engineers, (ASHRAE): New York, NY, USA, 2020.
- Fanger, P.O. Thermal Comfort. Analysis and Applications in Environmental Engineering, 1st ed.; Danish Technical Press: Copenhagen, Denmark, 1970. [Google Scholar]
- ISO 7730; Ergonomics of the Thermal Environment—Analytical Determination and Interpretation of Thermal Comfort Using Calculation of the PMV and PPD Indices and Local Thermal Comfort Criteria 2005. The International Organization for Standardization: Geneva, Switzerland, 2005.
- Rupp, R.F.; Parkinson, T.; Kim, J.; Toftum, J.; de Dear, R. The impact of occupant’s thermal sensitivity on adaptive thermal comfort model. Build. Environ. 2022, 207, 108517. [Google Scholar] [CrossRef]
- Jeong, B.; Kim, J.; Chen, D.; de Dear, R. Comparison of residential thermal comfort in two different climates in Australia. Build. Environ. 2022, 211, 108706. [Google Scholar] [CrossRef]
- Brager, G.S.; de Dear, R. Thermal adaptation in the built environment: A literature review. Energy Build. 1998, 27, 83–96. [Google Scholar] [CrossRef] [Green Version]
- Nicol, J.F.; Humphreys, M.A. Adaptive thermal comfort and sustainable thermal standards for buildings. Energy Build. 2002, 34, 563–572. [Google Scholar] [CrossRef]
- McNair, D.M.; Lorr, M.; Droppleman, L.F. Manual for the Profile of Mood States; Educational and Industrial Testing Services: San Diego, CA, USA, 1974. [Google Scholar]
- Gagge, A.P.; Burton, A.C.; Bazett, H.C. A Practical System of Units for the Description of the Heat Exchange of Man with His Environment. Sci. New Ser. 1941, 94, 428–430. [Google Scholar] [CrossRef] [PubMed]
- Gagge, A.P.; Stolwijk, J.A.J.; Nishi, Y. An Effective Temperature Scale Based on a Simple Model of Human Physiological Regulatory Response. ASHRAE Trans. 1971, 77, 21–36. [Google Scholar]
- Taniguchi, Y.; Aoki, H.; Fujikake, K.; Tanaka, H.; Kitada, M. Study on Car Air Conditioning System Controlled by Car Occupants’ Skin Temperatures—Part 1: Research on a Method of Quantitative Evaluation of Car Occupants’ Thermal Sensations by Skin Temperatures. SAE Tech. Pap. 1992, 920169. [Google Scholar]
- Yosida, A.; Hashida, S.; Kinoshita, S. Thermal Environment and Mental State in Premises Woods in Urban Tokyo Area. J. Heat Isl. Inst. Int. 2017, 12, 115–121. [Google Scholar]
- de Dear, R.J.; Kim, J.; Parkinson, T. Residential adaptive comfort in a humid subtropical climate—Sydney Australia. Energy Build. 2018, 158, 1296–1305. [Google Scholar] [CrossRef]
- de Dear, R.J.; Brager, G.S. Thermal comfort in naturally ventilated buildings: Revisions to ASHRAE Standard 55. Energy Build. 2002, 34, 549–561. [Google Scholar] [CrossRef] [Green Version]
- Lala, B.; Biju, A.; Vanshita; Rastogi, A.; Dahiya, K.; Kala, S.M.; Hagishima, A. The Challenge of Multiple Thermal Comfort Prediction Models: Is TSV Enough? Buildings 2023, 13, 890. [Google Scholar] [CrossRef]
- Rohles, F. Temperature & Temperament. A Psychologist Looks at Comfort. ASHRAE Trans. 2007, 49, 14–19. [Google Scholar]
- Nikolopoulou, M.; Baker, N.; Steemers, K. Thermal comfort in outdoor urban spaces: Understanding the human parameter. Sol. Energy 2001, 70, 227–235. [Google Scholar] [CrossRef]
- Zabetian, E.; Kheyroddin, R. Comparative evaluation of relationship between psychological adaptations in order to reach thermal comfort and sense of place in urban spaces. Urban Clim. 2019, 29, 100483. [Google Scholar] [CrossRef]
- Höppe, P. Different aspects of assessing indoor and outdoor thermal comfort. Energy Build. 2002, 34, 661–665. [Google Scholar] [CrossRef]
- Zrudlo, L.R. A climatic approach to town planning in the Arctic. Energy Build. 1988, 11, 41–63. [Google Scholar] [CrossRef]
- Huizenga, C.; Zhang, H.; Arens, E.; Wang, D. Skin and core temperature response to partial-and whole-body heating and cooling. J. Therm. Biol. 2004, 29, 549–558. [Google Scholar] [CrossRef] [Green Version]
- Wu, Z.; Li, N.; Cui, H.; Peng, J.; Chen, H.; Liu, P. Using Upper Extremity Skin Temperatures to Assess Thermal Comfort in Office Buildings in Changsha, China. Int. J. Environ. Res. Public. Health 2017, 14, 1092. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Humphreys, M.A.; McCartney, K.J.; Nicol, J.F.; Raja, I.A. An analysis of some observations of the finger temperature and thermal comfort of office workers. In Proceedings of the Indoor Air, Edinburg, UK, 8–13 August 1999. [Google Scholar]
- Yao, Y.; Lian, Z.; Liu, W.; Shen, Q. Experimental Study on Skin Temperature and Thermal Comfort of the Human Body in a Recumbent Posture under Uniform Thermal Environments. Indoor Built Environ. 2007, 16, 505–518. [Google Scholar] [CrossRef]
- Liu, W.; Lian, Z.; Liu, Y. Heart rate variability at different thermal comfort levels. Eur. J. Appl. Physiol. 2008, 103, 361–366. [Google Scholar] [CrossRef]
- Uemae, T.; Uemae, M.; Kamijo, M. Evaluation of Psychological and Physiological Responses Under Gradual Change of Thermal Conditions with Aim to Create Index to Evaluate Thermal Comfort of Clothes. In Proceedings of the 7th International Conference on Kansei Engineering and Emotion Research, Kuching, Malaysia, 19–22 March 2018. [Google Scholar]
- Ishigaki, H.; Matsubara, T.; Gonda, S.; Horikoshi, T. Experimental trial on the seasonal differences of the combined effect of air temperature and humidity on the human physiological and psychological responses. J. Struct. Constr. Eng. 2001, 543, 49–56. [Google Scholar]
- Wu, Y.; Hong, L.; Baizhan, L.; Risto, K.; Deyu, K.; Shan, Z.; Yao, R. Thermal adaptation of the elderly during summer in a hot humid area: Psychological, behavioral, and physiological responses. Energy Build. 2019, 203, 109450. [Google Scholar] [CrossRef]
- Ibrahim, A.; Ali, H.; Zghoul, A.; Jaradat, S. Mood state and human evaluation of the thermal environment using virtual settings. Indoor Built Environ. 2021, 30, 70–86. [Google Scholar] [CrossRef]
- Lin, S.; Hsiao, Y.Y.; Wang, M. Test Review: The Profile of Mood States 2nd Edition. J. Psychoeduc. Assess. 2014, 32, 273–277. [Google Scholar] [CrossRef]
- Turhan, C.; Özbey, M.F. Effect of pre-and post-exam stress levels on thermal sensation of students. Energy Build. 2021, 231, 110595. [Google Scholar] [CrossRef]
- Kottek, M.; Grieser, J.; Beck, C.; Rudolf, B.; Rubel, F. World map of the Köppen-Geiger climate classification updated. Meteorol. Z. 2006, 15, 259–263. [Google Scholar] [CrossRef] [PubMed]
- Turkish State Meteorological Service. Available online: https://mgm.gov.tr/eng/forecast-cities.aspx (accessed on 6 March 2023).
- Anand, A.; Li, Y.; Wang, Y.; Gardner, K.; Lowe, M.J. Reciprocal effects of antidepressant treatment on activity and connectivity of the mood regulating circuit: An FMRI study. J. Neuropsychiatry Clin. Neurosci. 2007, 19, 274–282. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lara, D.R.; Bisol, L.W.; Munari, L.R. Antidepressant, mood stabilizing and precognitive effects of very low dose sublingual ketamine in refractory unipolar and bipolar depression. Int. J. Neuropsychopharmacol. 2013, 16, 2111–2117. [Google Scholar] [CrossRef] [Green Version]
- Thermal Comfort Data Logger HD32.3TC—HD32.3TCA—Thermal Microclimate PMV-PPD/WBGT, Delta OHM, Italy. Available online: https://www.deltaohm.com/wp-content/uploads/document/DeltaOHM_HD32.3TC_datasheet_ENG.pdf (accessed on 21 April 2023).
- ISO 7726; Ergonomics of the Thermal Environment, Instruments for Measuring Physical Quantities. International Standard Organization (ISO): Geneva, Switzerland, 1998.
- ISO 7243; Ergonomics of the Thermal Environment—Assessment of Heat Stress Using the WBGT (Wet Bulb Globe Temperature) Index. International Standard Organization (ISO): Geneva, Switzerland, 2017.
- Turhan, C.; Akkurt, G.G. The relation between thermal comfort and human-body exergy consumption in a temperate climate zone. Energy Build. 2019, 205, 109548. [Google Scholar] [CrossRef]
- DHT 22 Digital-Output Relative Humidity & Temperature Sensor, Aosong Electronics Co., Ltd., China. Available online: https://www.sparkfun.com/datasheets/Sensors/Temperature/DHT22.pdf (accessed on 6 March 2023).
- Buratti, C.; Palladino, D.; Ricciardi, P. Application of a new 13-value thermal comfort scale to moderate environments. Appl. Energy 2016, 180, 859–866. [Google Scholar] [CrossRef]
- Buratti, C.; Ricciardi, P. Adaptive analysis of thermal comfort in university classrooms: Correlation between experimental data and mathematical models. Build. Environ. 2009, 44, 674–687. [Google Scholar] [CrossRef]
- Terry, P.C.; Lane, A.M. Normative values for the profile of mood states for use with athletic samples. J. Appl. Sport Psychol. 2000, 12, 93–109. [Google Scholar] [CrossRef] [Green Version]
- Thangaleela, S.; Sivamaruthi, B.S.; Kesika, P.; Chaiyasut, C. Role of Probiotics and Diet in the Management of Neurological Diseases and Mood States: A Review. Microorganisms 2022, 10, 2268. [Google Scholar] [CrossRef]
- Singh, M.K.; Mahapatra, S.; Teller, J. Relation between indoor thermal environment and renovation in Liege residential buildings. Therm. Sci. 2014, 18, 889–902. [Google Scholar] [CrossRef] [Green Version]
- Van der Linden, A.C.; Boerstra, A.C.; Raue, A.K.; Kurvers, S.R.; de Dear, R.J. Adaptive temperature limits: A new guideline in The Netherlands: A new approach for the assessment of building performance with respect to thermal indoor climate. Energy Build. 2006, 38, 8–17. [Google Scholar] [CrossRef]
- McCartney, K.J.; Nicol, F.J. Developing an Adaptive Control Algorithm for Europe. Energy Build. 2002, 34, 623–635. [Google Scholar] [CrossRef]
- MATLAB, v2020a. The Math Works, Inc. Available online: www.mathworks.com (accessed on 6 March 2023).
- Fan, C.; Ding, Y. Cooling load prediction and optimal operation of HVAC systems using a multiple nonlinear regression model. Energy Build. 2019, 197, 7–17. [Google Scholar] [CrossRef]
- Yasar, A.; Bilgili, M.; Simsek, E. Water demand forecasting based on stepwise multiple nonlinear regression analysis. Arab. J. Sci. Eng. 2012, 37, 2333–2341. [Google Scholar] [CrossRef]
- Shrestha, M.; Rijal, H.B. Investigation on Summer Thermal Comfort and Passive Thermal Improvements in Naturally Ventilated Nepalese School Buildings. Energies 2023, 16, 1251. [Google Scholar] [CrossRef]
- de Dear, R.J.; Brager, G.S. Towards an adaptive model of thermal comfort and preference. ASHRAE Trans. 1998, 104, 145–167. [Google Scholar]
- Humphreys, M.A.; Nicol, J.F. Outdoor temperature and indoor thermal comfort: Raising the precision of the relationship for the 1998 ASHRAE database of field studies. ASHRAE Trans. 2000, 106, 485–492. [Google Scholar]
- Yao, R.; Li, B.; Liu, J. A theoretical adaptive model of thermal comfort–Adaptive Predicted Mean Vote (aPMV). Build. Environ. 2009, 44, 2089–2096. [Google Scholar] [CrossRef]
- Bunge, M. A general black box theory. Philos. Sci. 1963, 30, 346–358. [Google Scholar] [CrossRef]
- Martins, A.; Fonseca, I.; Farinha, J.T.; Reis, J.; Cardoso, A.J.M. Online Monitoring of Sensor Calibration Status to Support Condition-Based Maintenance. Sensors 2023, 23, 2402. [Google Scholar] [CrossRef]
- Topçu, S.; Incecik, S.; Atimtay, A.T. Chemical composition of rainwater at EMEP station in Ankara, Turkey. Atmos. Res. 2002, 65, 77–92. [Google Scholar] [CrossRef]
- Yatin, M.; Tuncel, S.; Aras, N.K.; Olmez, I.; Aygun, S.; Tuncel, G. Atmospheric trace elements in Ankara, Turkey: 1. Factors affecting chemical composition of fine particles. Atmos. Environ. 2000, 34, 1305–1318. [Google Scholar] [CrossRef]
- Morgan, W.P. Selected psychological factors limiting performance: A mental health model. In Limits of Human Performance; Clarke, D.H., Eckert, H.M., Eds.; Human Kinetics: Champaign, IL, USA, 1985; pp. 70–80. [Google Scholar]
Temperature and Relative-Humidity Sensor | DHT-22 [46] | RH range | 0–100% | |
Temperature range | −40 to 80 °C | |||
RH accuracy | ±3% (Max ± 5%) | |||
Temperature accuracy | <± 0.5 °C | |||
Thermal Comfort Data Logger | DELTA OHM HD32.3TCA [42] | Globe temperature | Type of probe | TP3276.2 probe |
Measuring range | −10 to 100 °C | |||
Accuracy | ±0.1 °C | |||
Resolution | 0.1 °C | |||
Air velocity | Type of probe | AP3203.2 probe | ||
Measuring range | 0.02 to 5 m/s | |||
Accuracy | ±(0.05 + 5% of the measurement) m/s | |||
Resolution | 0.01 m/s | |||
Air temperature and Relative humidity | Type of probe | HP3217.2R probe | ||
Measuring range | Temperature: −40 to 100 °C RH: 0–100% | |||
Accuracy | Temperature: ±0.1 °C RH: ±1.5% | |||
Resolution | Temperature: 0.1 °C RH:0.1% |
T-Score | Classification of Mood State |
---|---|
70+ | Very Elevated Score—Very Pessimistic |
(Many more concerns than are typically reported) | |
60–69 | Elevated Score—Pessimistic |
(More concerns than are typically reported) | |
40–59 | Average Score—Neutral |
(Typical levels of concern) | |
30–39 | Low Score—Optimistic |
(Fewer concerns than are typically reported) | |
<30 | Very Low Score—Very Optimistic |
(Far fewer concerns than are typically reported) |
Winter Season | Summer Season | ||||
---|---|---|---|---|---|
Mean | Standard Deviation | Mean | Standard Deviation | ||
Tout | °C | 8.8 | 6.5 | 22.4 | 4.6 |
Ta | °C | 21.4 | 1.1 | 23.1 | 1.2 |
Tr | °C | 21.5 | 0.9 | 23.0 | 2.5 |
RHi | % | 34.1 | 8.2 | 38.5 | 6.5 |
RHo | % | 66.1 | 18.3 | 38.3 | 13.5 |
va | m/s | <0.01 | 0.001 | <0.01 | 0.001 |
PMV | - | −0.48 | 0.24 | −0.11 | 0.31 |
AMV | - | 0.20 | 0.89 | 0.42 | 1.01 |
Icl | - | 0.95 | 0.27 | 0.56 | 0.21 |
met | - | 1.1 | - | 1.1 | - |
Min–Max * | Mean | SD | |
---|---|---|---|
Tension-Anxiety (TA) | [0;36] | 14.19 | 5.96 |
Depression-Dejection (DD) | [0;60] | 15.95 | 9.86 |
Anger-Hostility (AH) | [0;36] | 14.05 | 8.03 |
Vigor-Activity (VA) | [0;32] | 15.76 | 5.01 |
Fatigue-Inertia (FI) | [0;28] | 12.39 | 5.27 |
Confusion-Bewilderment (CB) | [0;32] | 9.78 | 4.06 |
Classification of TMD | MSCF Values |
---|---|
Very Elevated Score—Very Pessimistic (Many more concerns than are typically reported) | −0.125 |
Elevated Score—Pessimistic (More concerns than are typically reported) | −0.075 |
Average Score—Neutral (Typical levels of concern) | 0 |
Low Score—Optimistic (Fewer concerns than are typically reported) | −0.061 |
Very Low Score—Very Optimistic (Far fewer concerns than are typically reported) | −0.114 |
Mood State | MSCF Values | AMVp Values |
---|---|---|
Very Pessimistic | −0.125 | 0.537 |
Pessimistic | −0.075 | 0.519 |
Neutral | 0 | 0.5 |
Optimistic | −0.061 | 0.515 |
Very Optimistic | −0.114 | 0.53 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Turhan, C.; Özbey, M.F.; Çeter, A.E.; Akkurt, G.G. A Novel Data-Driven Model for the Effect of Mood State on Thermal Sensation. Buildings 2023, 13, 1662. https://doi.org/10.3390/buildings13071662
Turhan C, Özbey MF, Çeter AE, Akkurt GG. A Novel Data-Driven Model for the Effect of Mood State on Thermal Sensation. Buildings. 2023; 13(7):1662. https://doi.org/10.3390/buildings13071662
Chicago/Turabian StyleTurhan, Cihan, Mehmet Furkan Özbey, Aydın Ege Çeter, and Gulden Gokcen Akkurt. 2023. "A Novel Data-Driven Model for the Effect of Mood State on Thermal Sensation" Buildings 13, no. 7: 1662. https://doi.org/10.3390/buildings13071662