A Comprehensive Survey about Thermal Comfort under the IoT Paradigm: Is Crowdsensing the New Horizon?
<p>Normalized Google trends of the queries “Smart home”, “Alexa” and “Nest”.</p> "> Figure 2
<p>Trends in Google Shopping of the queries “Nest”, “Honeywell”, “Ecobee”, “Samsung home” and “Siemens home”. From Google Trends.</p> "> Figure 3
<p>Trends of studies according to Scopus in the last decade. Queries: “Crowdsensing”, “Internet of things objects”, “EnergyPlus”.</p> "> Figure 4
<p>Trends of studies according to Scopus in the last decade. Queries: “Crowdsensing + thermal comfort”, “EnergyPlus + thermal comfort”, “Internet of things + thermal comfort”, “sensing + thermal comfort + Internet of Things” and “sensing + thermal comfort + indoor”.</p> "> Figure 5
<p>Tree of main topics based on thermal comfort in the last decade.</p> ">
Abstract
:1. Introduction
1.1. Context
- Sensing or Perception Layer, dedicated to the acquisition of information.
- Network Layer, that connect data and manage the control centre.
- Application Layer, that is supposed to achieve the energy management.
1.2. Paper’s Structure
2. Thermal Comfort Studies with IoT Hardware
2.1. Strategies Involving IoT Technologies to Produce Changes in the Indoor Environment
2.2. IoT Devices to Modify the Micro-Climate of Each Individual and Enhance Thermal Comfort
2.3. Studies with Monitoring Purpose Using IoT Technologies
2.4. Demonstration Studies
3. Building Simulation Models under the IoT Paradigm for the Study of Thermal Comfort
3.1. Evaluation of Thermal Comfort under Modifications of the Physical Characteristics of the Building
3.2. Evaluation of Thermal Comfort Under Modifications of Users’ Behaviour
3.3. Combined Platforms
3.4. Response of Thermal Comfort to Future Overheating
4. Mobile Crowdsensing
- Today’s mobile devices have better resources in terms of communication, computing and storage than mote-class.
- People already use these devices whatever they do.
- Human intelligence can help to collect higher quality and more complex data than machine software.
- The incentive mechanisms behind this kind of application are normally strong.
4.1. Making Mobile Crowdsensing Work
4.2. Crowdsensing for Thermal Comfort
5. Quantitative Analysis of Scientific Community’s Contribution
6. Discussion
7. Conclusions and Future Work
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Source | Smart Objects | Thermal Comfort Model | Mathematical Model | Heating versus Cooling | Availability | Context | Sample | Geographic Location | Duration | Thermal Satisfaction | Energy Savings |
---|---|---|---|---|---|---|---|---|---|---|---|
Knecht et al. (2016) [54] | Personal devices, mostly garments | Adaptive | Inductive approach proposed by Braun and Clark (2006) | Both | Commercially available | Open-plan offices (university) | 6 (heating) 8 (cooling) | London, UK | 4 weeks (Mar) and 4 weeks (Jul) | Not quantified | Not quantified |
Zhang et al. (2015) [55] | Footwarmers | ASHRAE Standard 55 | Berkeley Simple Measurement and Actuation Profile | Heating | Fabricated by the authors | Office Workplace (university library) | 16 | Berkeley, California, USA | Small periods during half a year | 80–100% | 37–75% depending on the outdoor temperature |
Feldmeier and Paradiso (2010) [49] | Smart HVAC, wearable wrist devices, sensors, control nodes | PMV Model (with minor modifications) | Hybridized control system | Cooling | Circuit boards fabricated by authors | Workspace (university) | 10 | Cambridge, Massachusetts, USA | Three months (May–Aug) | More than 80% | Up to 24% over the previous HVAC control system |
Salamone et al. (2018) [57] | Wristband and nearable devices (sensors) | PMV model and adaptive model | Grasshopper, Python, Machine Learning | Heating | Commercially available | Office building | 8 | Milan, Italy | Small periods over 3 weeks (Nov) | Not quantified | Not quantified |
Sung et al. (2019) [50] | Smart HVAC, sensors | PMV Model | Matlab simulation, Machine Learning | Cooling | Commercially available | Workspace | 12 (simulated) | Taiwan | Not specified | 83% | 6–11.3% over the “comfort mode” (estimated) |
Kim et al. (2018) [56] | Smart chairs | New PCS model | Machine Learning | Both | Fabricated by the authors | Office (university) | 38 | Berkeley, California, USA | 7 months (Apr to Oct) | Not quantified | Not quantified |
Source | Thermal Comfort Model | Smart Objects | Mathematical Model | Context | Sample | Geographic Location | Heating/Cooling | Accuracy |
---|---|---|---|---|---|---|---|---|
Dai et al. (2017) [60] | ASHRAE Standard 55 | Fibre Bragg grating-based sensors | SVM classifier, Machine Learning | Controlled Environmental Chamber + private office | 11 (experiment 1) + 1 (experiment 2) | Berkeley, California, USA (exp 1) + Shanghai, China (exp 2) | Both | Over 80% (to 90% with 28 samples and three-inputs model) |
Choi et al. (2017) [64] | ASHRAE –PMV survey designation | Exposed thermistor-type skin sensors | Excel, Minitab, stepwise regression | Experimental chamber at University of Southern California | 18 (11 males and 7 females) | Los Angeles, California, USA | Both | 95.87% with 3 body parts, 94.39% with one body area and the changing rate |
Ghahramani et al. (2018) [59] | ASHRAE standard requirements | Infrared sensing system | Hidden Markov Model-based learning method | Shared office space university building | 10 | Los Angeles, California, USA | Both | 82.8% |
Source | Thermal Comfort Model | Heating/Cooling | Context | Geographic Location | Sample | Duration | Changing Proposal | Energy-Saving |
---|---|---|---|---|---|---|---|---|
Ashrafian et al. (2019) [68] | PMV model | Heating | Classrooms | Eskisehir, Turkey | 12 classrooms | 2 academic semesters | Preliminary design stage | 8.5% |
Escandón et al. (2019) [84] | Adaptive model | Both | Social housing | Seville, Spain | 2 people | One year | Retrofit strategies | Not defined |
Ramallo-González et al. (2019) [85] | Adaptive model | Both | University campus | Murcia, Spain | 13 thermal zones | 1 year (data collection) | Behavioural modification | 41% Heating, 8.3% Cooling |
Esteves et al. (2019) [74] | PMV model | Heating | Cinema Room (mechanically ventilated) | Penafiel, Portugal | 6041 people (simulated) | 2 months (Dec–Jan) | No changing proposed | Not quantifiable |
Jeanblanc et al. (2016) [77] | Adaptive model | Cooling | Research lab | Iowa, USA | 1 lab | 3 months (Jun to Aug) | Natural ventilation | Up to 83% |
Kinnane et al. (2016) [80] | PMV Model | Heating | Dementia-friendly dwellings | Dublin, Ireland | 5 people (aged between 79 and 82) | Not specified | Personal control | Not quantifiable |
Kwok et al. (2018) [75] | ePMV model | Cooling | High-rise residential building | Hong Kong, China | Simulated occupant density of 0.083 people/m2 | Not specified | Natural ventilation | Not quantifiable |
Nouvel and Alessi (2012) | PMV model | Both | Office building | Lyon, France | 2 people (simulated) | 1 week (summer) + 1 week (winter) | HVAC control architecture | 57% (summer); 22% (winter) |
Oliveira and Labaki (2016) [83] | Adaptive model | Cooling | University campus | Campinas, Brazil | 1 office room | 8 months (Dec to Aug) | Solar chimney | Not quantifiable |
Pellegrino et al. (2016) [78] | “Model-free” approach | Cooling | Dwellings (naturally ventilated with a ceiling fans) | Kolkata, India | 2 dwellings | 1 month | Low-cost strategies and behavioural modifications | 35% (flat 1), 76% (flat 2) |
Rincón et al. (2019) [73] | Adaptive model | Both | Dwelling (naturally ventilated) | Burkina Faso, Africa | 2 people (simulated) | 3 weeks (one in Dec, one in Jun, one in Apr) | Passive strategies | Not quantifiable |
Thravalou et al. (2016) [79] | Adaptive model | Cooling | Vernacular building | Nicosia, Cyprus | 1 building | 2 months (Jul–Aug) | Passive strategies | Not quantifiable |
Yun et al. (2018) [76] | Adaptive model | Cooling | University building (mixed-mode condition) | Suwon, South Korea | 77 people | 3 months (Jul to Sep) | Perceived control | 9% |
Zhao et al. (2016) [81] | PMV model | Heating | Office building (mixed-mode) | Pittsburgh, Pennsylvania, USA | 15 people | 3 months (Oct to Dec) | Active HVAC control | Up to 61.20% |
Source | Thermal Comfort Model | Vote Scale | Heating versus Cooling | Context | Sample | Geographic Location | Duration | Changing Proposal | Energy Savings | Thermal Satisfaction |
---|---|---|---|---|---|---|---|---|---|---|
Lam and Wang (2013) [111] | PMV model | ASHRAE 7-point scale | Cooling | Commercial building and university | 11 people (commercial building) + 12 (university) | Hong Kong, China | 3 weeks (commercial building) + 4 weeks (university) | Optimised set-point | 13% | 28.2% |
Cottafava et al. (2019) [107] | Adaptive model and PMV model | 5-point scale designed by authors | Both | Classrooms and offices | Not specified | Turin, Italy | 9 months | Optimised set-point | Up to 54% | From 1.7 to 2.7 on a 1–5 scale |
Jazizadeh et al. (2014) [115] | PMV model | Thermal Preference (TP) scale, designed by authors (from −50 to + 50) | Cooling | Office Building | 4 people and 7 simulated | Southern California, USA | 2 months | Optimised set-point | Not quantified | Not quantified |
Li et al. (2017) [114] | “Model-free” approach | Thermal sensation: 5-point scale, Thermal preference: 3-point scale | Both | Workplace, single-occupancy rooms | 7 (office), 3 (rooms) | Wisconsin (office), Michigan (rooms), USA | 3 weeks winter (office), 6 weeks summer (rooms) | Optimised set-point | Not quantified | Estimated reduction uncomfortable reports: 53.7% |
Sanguinetti et al. (2017) [108] | PMV model | 5-point scale designed by authors (ASHRAE inspired) | Both | University campus | 4300 users | Davis, California, USA | 23 months | Optimised set-point | 20–30% | 11–40% |
Erickson and Cerpa (2012) [35] | PMV model | ASHRAE 7-point scale | Heating | LEED Gold-Certified Building | 39 participants | Merced, California, USA | 5 weeks | Optimised set-point | 10.1% | 80% “satisfied” or “somewhat satisfied”, 13% “neutral” |
Sood et al. (2019) [112] | PMV model | 3-point scale designed by authors | Cooling | Net Zero Energy Building | 616 users | Singapore | 3 months | None (monitoring aim) | Not quantified | Not quantified |
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Tomat, V.; Ramallo-González, A.P.; Skarmeta Gómez, A.F. A Comprehensive Survey about Thermal Comfort under the IoT Paradigm: Is Crowdsensing the New Horizon? Sensors 2020, 20, 4647. https://doi.org/10.3390/s20164647
Tomat V, Ramallo-González AP, Skarmeta Gómez AF. A Comprehensive Survey about Thermal Comfort under the IoT Paradigm: Is Crowdsensing the New Horizon? Sensors. 2020; 20(16):4647. https://doi.org/10.3390/s20164647
Chicago/Turabian StyleTomat, Valentina, Alfonso P. Ramallo-González, and Antonio F. Skarmeta Gómez. 2020. "A Comprehensive Survey about Thermal Comfort under the IoT Paradigm: Is Crowdsensing the New Horizon?" Sensors 20, no. 16: 4647. https://doi.org/10.3390/s20164647
APA StyleTomat, V., Ramallo-González, A. P., & Skarmeta Gómez, A. F. (2020). A Comprehensive Survey about Thermal Comfort under the IoT Paradigm: Is Crowdsensing the New Horizon? Sensors, 20(16), 4647. https://doi.org/10.3390/s20164647