Abstract
The personal comfort system (PCS) aims to meet individual thermal comfort demands efficiently to achieve higher thermal comfort satisfaction while reducing air conditioning energy consumption. To date, many PCS devices have been developed and evaluated from the perspective of thermal comfort. It will be useful for future PCS development if an approach to quantify the thermal comfort and energy performance of certain PCS devices and their combinations with consideration of user behaviors can be established. This study attempted to fill this gap by integrating thermal comfort experiments, occupancy simulations, usage behavior modeling, and building energy simulation technologies. First, human subject experiments were conducted to quantify the thermal comfort effects of the PCS. Then, the Markov chain model and conditional probability model were employed to describe the room occupancy and PCS usage behaviors. Finally, the extended comfort temperature range and user behavior models were imported into the building energy simulation tool to analyze the energy-saving potential of the PCS. The results show that the use of PCS can significantly improve occupants’ thermal comfort and satisfaction rate under both warm and cool conditions. Using a cooling cushion and desktop fan can lift the upper limit of the comfortable temperature to 29.5 °C while the heated cushion can extend the lower limit to 15 °C. By increasing the air conditioning temperature setpoint by 2 °C in summer and reducing by 2.5 °C the heating temperature setpoint in winter, PCS devices can reduce heating and air conditioning energy consumption by 25%–40% while maintaining occupants’ thermal comfort.
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References
ASHRAE (2010). Standard 55 Thermal Environmental Conditions for Human Occupancy. Atlanta: American Society of Heating, Refrigeration and Air-Conditioning Engineers.
Bauman F, Raftery P, Kim J, et al. (2017). Changing the rules: Innovative low-energy occupant-responsive HVAC controls and systems. UC Berkeley, Center for the Built Environment, USA. Available at https://escholarship.org/uc/item/23t9k6rm
Chakroun W, Ghaddar N, Ghali K (2011). Chilled ceiling and displacement ventilation aided with personalized evaporative cooler. Energy and Buildings, 43: 3250–3257.
Chang WK, Hong T (2013). Statistical analysis and modeling of occupancy patterns in open-plan offices using measured lighting-switch data. Building Simulation, 6: 23–32.
D’Oca S, Fabi V, Corgnati SP, et al. (2014). Effect of thermostat and window opening occupant behavior models on energy use in homes. Building Simulation, 7: 683–694.
DOE (2011). Quadrennial technology review. U.S. Department of Engery.
Ding Y, Han S, Tian Z, et al. (2021). Review on occupancy detection and prediction in building simulation. Building Simulation, https://doi.org/10.1007/s12273-021-0813-8.
Dong B, Yan D, Li Z, et al. (2018). Modeling occupancy and behavior for better building design and operation—A critical review. Building Simulation, 11: 899–921.
EIA (2020). Annual energy outlook. Energy Information Administration, USA. Available at https://www.eia.gov/outlooks/aeo/
Erwin J (2017). Cool people: Wearable and personal comfort products that will contribute to an overall energy efficiency strategy. In: Proceedings of the International Conference on Energy Efficiency in Lighting and Domestic Appliances, Irvine, CA, USA.
Foda E, Sirén K (2012). Design strategy for maximizing the energy-efficiency of a localized floor-heating system using a thermal manikin with human thermoregulatory control. Energy and Buildings, 51: 111–121.
Hong T, Chen Y, Belafi Z, et al. (2018). Occupant behavior models: A critical review of implementation and representation approaches in building performance simulation programs. Building Simulation, 11: 1–14.
Hoyt T, Lee KH, Zhang H, et al. (2009). Energy savings from extended air temperature setpoints and reductions in room air mixing. In: Proceedings of the International Conference on Environmental Ergonomics, Boston, MA, USA.
Huang L, Kang J (2021). Thermal comfort in winter incorporating solar radiation effects at high altitudes and performance of improved passive solar design—Case of Lhasa. Building Simulation, 14: 1633–1650.
Huizenga C, Abbaszadeh S, Zagreus L, et al. (2006). Air quality and thermal comfort in office buildings: Results of a large indoor environmental quality survey. In: Proceedings of the Healthy Buildings 2006, Lisbon, Portugal.
Jian Y, Guo Y, Liu J, et al. (2011). Case study of window opening behavior using field measurement results. Building Simulation, 4: 107–116.
Jin Y, Yan D, Zhang X, et al. (2021). A data-driven model predictive control for lighting system based on historical occupancy in an office building: Methodology development. Building Simulation, 14: 219–235.
Kim J, Bauman F, Raftery P, et al. (2019). Occupant comfort and behavior: High-resolution data from a 6-month field study of personal comfort systems with 37 real office workers. Building and Environment, 148: 348–360.
Li Z, Zhu H, Dong B, et al. (2021). Development of a systematic procedure to establish customized shading behavior identification model. Energy and Buildings, 239: 110793.
Luo M, Arens E, Zhang H, et al. (2018). Thermal comfort evaluated for combinations of energy-efficient personal heating and cooling devices. Building and Environment, 143: 206–216.
Makhoul A, Ghali K, Ghaddar N (2013a). Desk fans for the control of the convection flow around occupants using ceiling mounted personalized ventilation. Building and Environment, 59: 336–348.
Makhoul A, Ghali K, Ghaddar N (2013b). Thermal comfort and energy performance of a low-mixing ceiling-mounted personalized ventilator system. Building and Environment, 60: 126–136.
Oi H, Yanagi K, Tabata K, et al. (2011). Effects of heated seat and foot heater on thermal comfort and heater energy consumption in vehicle. Ergonomics, 54: 690–699.
Pan CS, Chiang HC, Yen MC, et al. (2005). Thermal comfort and energy saving of a personalized PFCU air-conditioning system. Energy and Buildings, 37: 443–449.
Pan S, Han Y, Wei S, et al. (2019). A model based on Gauss distribution for predicting window behavior in building. Building and Environment, 149: 210–219.
Pasut W, Zhang H, Arens E, et al. (2015). Energy-efficient comfort with a heated/cooled chair: Results from human subject tests. Building and Environment, 84: 10–21.
Ren X, Yan D, Wang C (2014). Air-conditioning usage conditional probability model for residential buildings. Building and Environment, 81: 172–182.
Schiavon S, Melikov AK (2008). Energy saving and improved comfort by increased air movement. Energy and Buildings, 40: 1954–1960.
Schiavon S, Melikov AK, Sekhar C (2010). Energy analysis of the personalized ventilation system in hot and humid climates. Energy and Buildings, 42: 699–707.
Shahzad S, Calautit JK, Aquino AI, et al. (2017). A user-controlled thermal chair for an open plan workplace: CFD and field studies of thermal comfort performance. Applied Energy, 207: 283–293.
Tang J, Liu Y, Du H, et al. (2021a). The effects of portable cooling systems on thermal comfort and work performance in a hot environment. Building Simulation, 14: 1667–1683.
Tang R, Wang S, Sun S (2021b). Impacts of technology-guided occupant behavior on air-conditioning system control and building energy use. Building Simulation, 14: 209–217.
Tartarini F, Schiavon S, Cheung T, et al. (2020). CBE thermal comfort tool: Online tool for thermal comfort calculations and visualizations. Software X, 12: 100563.
Taub M, Zhang H, Arens E, et al. (2015). The use of footwarmers in offices for thermal comfort and energy savings in winter. UC Berkeley: Center for the Built Environment. Available at https://escholarship.org/uc/item/7vc2q28k
Veselý M, Zeiler W (2014). Personalized conditioning and its impact on thermal comfort and energy performance—A review. Renewable and Sustainable Energy Reviews, 34: 401–408.
Veselý M, Molenaar P, Vos M, et al. (2017). Personalized heating—Comparison of heaters and control modes. Building and Environment, 112: 223–232.
Wang C, Yan D, Jiang Y (2011). A novel approach for building occupancy simulation. Building Simulation, 4: 149–167.
Wang Z, de Dear R, Luo M, et al. (2018). Individual difference in thermal comfort: A literature review. Building and Environment, 138: 181–193.
Wang Z, Warren K, Luo M, et al. (2020). Evaluating the comfort of thermally dynamic wearable devices. Building and Environment, 167: 106443.
Xie J, Pan Y, Jia W, et al. (2019). Energy-consumption simulation of a distributed air-conditioning system integrated with occupant behavior. Applied Energy, 256: 113914.
Xiong W, Wang Y, Mathiesen BV, et al. (2015). Heat roadmap China: New heat strategy to reduce energy consumption towards 2030. Energy, 81: 274–285.
Yang B, Sekhar C, Melikov AK (2010). Ceiling mounted personalized ventilation system in hot and humid climate—An energy analysis. Energy and Buildings, 42: 2304–2308.
Zhai Y, Zhang H, Zhang Y, et al. (2013). Comfort under personally controlled air movement in warm and humid environments. Building and Environment, 65: 109–117.
Zhang YF, Wyon DP, Fang L, et al. (2007). The influence of heated or cooled seats on the acceptable ambient temperature range. Ergonomics, 50: 586–600.
Zhang H, Arens E, Kim D, et al. (2010). Comfort, perceived air quality, and work performance in a low-power task-ambient conditioning system. Building and Environment, 45: 29–39.
Zhang H, Arens E, Zhai Y (2015). A review of the corrective power of personal comfort systems in non-neutral ambient environments. Building and Environment, 91: 15–41.
Zhou X, Liu T, Yan D, et al. (2021). An action-based Markov chain modeling approach for predicting the window operating behavior in office spaces. Building Simulation, 14: 301–315.
Acknowledgements
This research has been supported by the National Natural Science Foundation of China (No. 51908414, No. 52108086), China National Key R&D Program during the 13th Five-year Plan Period (No. 2017YFC0702200).
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Zhang, J., Zhou, X., Lei, S. et al. Energy and comfort performance of occupant-centric air conditioning strategy in office buildings with personal comfort devices. Build. Simul. 15, 899–911 (2022). https://doi.org/10.1007/s12273-021-0852-1
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DOI: https://doi.org/10.1007/s12273-021-0852-1