Abstract
Enhancing the thermal comfort level of the occupants has been the subject of several research efforts focused on controlling the Heating, Ventilation and Air-Conditioning (HVAC) systems with the objective of higher occupant-thermal-comfort. It has been demonstrated that improving occupants’ thermal comfort often leads to savings in energy consumption. Also there are numerous studies that have directly aimed to optimize the energy consumption of the HVAC system while keeping the occupants’ thermal comfort within an acceptable range. In majority of the cases the level of control over the actions of the HVAC system is restricted to controlling the temperature set-point for the thermal zone. This study aims to explore the benefits of creating a more flexible HVAC system, which can lead to improvements in occupant thermal comfort and energy consumption of the HVAC system. The envisioned HVAC system will be capable of adjusting the direction of the airflow at each diffusor thereby producing a wider range of actions. In this study, a Computational Fluid Dynamic (CFD) simulation of a room was used as a proxy for the real-world environment, and the results of the CFD model were generalized through a Gaussian Process Regression (GPR) model to provide higher resolution data. The benefits of enabling the HVAC system to control the direction of airflow at the point of diffusion have been evaluated in terms of occupant’s thermal comfort and reduction in energy consumption.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
American Society of Heating, Refrigerating and Air Conditioning Engineers: ANSI/ASHRAE Standard 55-2013: Thermal Environmental Conditions for Human Occupancy. ASHRAE (2013)
US Energy Information Administration. http://www.eia.gov/tools/faqs/faq.php?id=86&t=1. Accessed 10 Jan 2018
US Department of Energy. https://energy.gov/public-services/homes/heating-cooling. Accessed 10 Jan 2018
US Energy Information Administration. https://www.eia.gov/consumption/commercial/reports/2012/energyusage. Accessed 10 Jan 2018
Federspiel, C.C., Asada, H.: User-adaptable comfort control for HVAC systems. J. Dyn. Syst. Meas. Control 116, 477–486 (1994)
Feldmeier, M., Paradiso, J.A.: Personalized HVAC control system. In: 2010 Internet of Things (IOT), pp. 1–8 (2010)
Erickson, V.L., Cerpa, A.E.: Thermovote. In: Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings - BuildSys 2012, pp. 9–16. ACM, Toronto (2012)
Gao, P.X., Keshav, S.: SPOT: a smart personalized office thermal control system. In: Proceedings of the Fourth International Conference on Future Energy Systems, pp. 237–246. ACM, Berkeley (2013)
Ghahramani, A., Jazizadeh, F., Becerik-Gerber, B.: A knowledge based approach for selecting energy-aware and comfort-driven HVAC temperature set points. Energy Build. 85, 536–548 (2014)
Jazizadeh, F., Pradeep, S.: Can computers visually quantify human thermal comfort?: Short Paper. In: Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments, pp. 95–98. ACM, Palo Alto (2016)
Jazizadeh, F., Ghahramani, A., Becerik-Gerber, B., Kichkaylo, T., Orosz, M.: User-led decentralized thermal comfort driven HVAC operations for improved efficiency in office buildings. Energy Build. 70, 398–410 (2014)
Mansourifard, P., Jazizadeh, F., Krishnamachari, B., Becerik-Gerber, B.: Online learning for personalized room-level thermal control: a multi-armed bandit framework. In: Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, pp. 1–8. ACM, Roma (2013)
Jazizadeh, F., Marin, F.M., Becerik-Gerber, B.: A thermal preference scale for personalized comfort profile identification via participatory sensing. Build. Environ. 68, 140–149 (2013)
Jazizadeh, F., Ghahramani, A., Becerik-Gerber, B., Kichkaylo, T., Orosz, M.: Personalized thermal comfort-driven control in HVAC-operated office buildings. In: 2013 Computing in Civil Engineering (2013)
Jazizadeh, F., Becerik-Gerber, B.: Toward adaptive comfort management in office buildings using participatory sensing for end user driven control. In: Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, pp. 1–8. ACM (2012)
Daum, D., Haldi, F., Morel, N.: A personalized measure of thermal comfort for building controls. Build. Environ. 46, 3–11 (2011)
Tachwali, Y., Refai, H., Fagan, J.E.: Minimizing HVAC energy consumption using a wireless sensor network. In: IECON 2007 - 33rd Annual Conference of the IEEE Industrial Electronics Society, pp. 439–444 (2007)
West, S.R., Ward, J.K., Wall, J.: Trial results from a model predictive control and optimisation system for commercial building HVAC. Energy Build. 72, 271–279 (2014)
Agarwal, Y., Balaji, B., Gupta, R., Lyles, J., Wei, M., Weng, T.: Occupancy-driven energy management for smart building automation. In: Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, pp. 1–6. ACM, Zurich (2010)
Agarwal, Y., Balaji, B., Dutta, S., Gupta, R.K., Weng, T.: Duty-cycling buildings aggressively: the next frontier in HVAC control. In: Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks, pp. 246–257 (2011)
Nassif, N., Kajl, S., Sabourin, R.: Evolutionary algorithms for multi-objective optimization in HVAC system control strategy. In: 2004 Processing IEEE Annual Meeting of the Fuzzy Information, NAFIPS 2004, vol. 51, pp. 51–56 (2004)
Fountain, M., Arens, E., de Dear, R., Bauman, F., Miura, K.: Locally controlled air movement preferred in warm isothermal environments (1994)
ANSYS Inc.: ANSYS Fluent 16.0 Theory Guide. ANSYS Inc. (2015)
Park, D., Battaglia, F.: Effect of heat loads and ambient conditions on thermal comfort for single-sided ventilation. Build. Simul. 8, 167–178 (2015)
Wang, J., Wang, S., Zhang, T., Battaglia, F.: Assessment of single-sided natural ventilation driven by buoyancy forces through variable window configurations. Energy Build. 139, 762–779 (2017)
Park, D., Battaglia, F.: Application of a wall-solar chimney for passive ventilation of dwellings. J. Solar Energy Eng. 137, 061006 (2015)
Rasmussen, C.E., William, C.K.I.: Gaussian Processes for Machine Learning. The MIT Press, Cambridge (2006)
Acknowledgement
This material is based upon work supported by the Institute for Critical Technology and Applied Science (ICTAS) at Virginia Tech. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the ICTAS.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Abedi, M., Jazizadeh, F., Huang, B., Battaglia, F. (2018). Smart HVAC Systems — Adjustable Airflow Direction. In: Smith, I., Domer, B. (eds) Advanced Computing Strategies for Engineering. EG-ICE 2018. Lecture Notes in Computer Science(), vol 10864. Springer, Cham. https://doi.org/10.1007/978-3-319-91638-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-91638-5_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91637-8
Online ISBN: 978-3-319-91638-5
eBook Packages: Computer ScienceComputer Science (R0)