IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v283y2021ics0306261920316421.html
   My bibliography  Save this article

How much HVAC energy could be saved from the occupant-centric smart home thermostat: A nationwide simulation study

Author

Listed:
  • Pang, Zhihong
  • Chen, Yan
  • Zhang, Jian
  • O'Neill, Zheng
  • Cheng, Hwakong
  • Dong, Bing

Abstract

Thermostat management plays a significant role in household energy conservation. This study aims to conduct a systematic and comprehensive analysis to quantify the energy savings potential of the occupant-centric smart thermostat based on a large-scale nationwide simulation infrastructure. The single-family Residential Prototype Building Model was used to represent a typical single-family detached house in the U.S. A generalized random occupancy presence schedule was created based on an occupancy probability schedule and k-means clustering algorithm. A total of 16,000 simulations, which were composed of four building foundation types, four heating source types, 40 American cities, five building energy code versions, and five thermostat control strategies, were conducted to evaluate the performances of the smart home thermostat in terms of saving building energy usage and maintaining occupant thermal comfort. The nationwide simulation results suggested that the temperature setback control during the unoccupied period could achieve some energy savings in the U.S. households. However, only very few of the 40 cities could see an annual Heating, Ventilation, and Air-conditioning energy savings ratio of over 30%. Besides, the implementation of the occupied standby temperature reset could greatly increase the peak load of the HVAC system and contribute to the grid load imbalance issue. It’s also worth noting that the smart recovery feature is proved to be able to bring additional benefits for a smart home thermostat. It could decrease the temperature setpoint not met time by about 30 min, and relieve the thermal discomfort due to the temperature setback control.

Suggested Citation

  • Pang, Zhihong & Chen, Yan & Zhang, Jian & O'Neill, Zheng & Cheng, Hwakong & Dong, Bing, 2021. "How much HVAC energy could be saved from the occupant-centric smart home thermostat: A nationwide simulation study," Applied Energy, Elsevier, vol. 283(C).
  • Handle: RePEc:eee:appene:v:283:y:2021:i:c:s0306261920316421
    DOI: 10.1016/j.apenergy.2020.116251
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920316421
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2020.116251?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ben-Nakhi, Abdullatif E. & Mahmoud, Mohamed A., 2002. "Energy conservation in buildings through efficient A/C control using neural networks," Applied Energy, Elsevier, vol. 73(1), pages 5-23, September.
    2. Jones, Glenn A. & Warner, Kevin J., 2016. "The 21st century population-energy-climate nexus," Energy Policy, Elsevier, vol. 93(C), pages 206-212.
    3. Ionescu, Constantin & Baracu, Tudor & Vlad, Gabriela-Elena & Necula, Horia & Badea, Adrian, 2015. "The historical evolution of the energy efficient buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 243-253.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fabio Gualandri & Aleksandra Kuzior, 2023. "Home Energy Management Systems Adoption Scenarios: The Case of Italy," Energies, MDPI, vol. 16(13), pages 1-20, June.
    2. Ozarisoy, B. & Altan, H., 2022. "Significance of occupancy patterns and habitual household adaptive behaviour on home-energy performance of post-war social-housing estate in the South-eastern Mediterranean climate: Energy policy desi," Energy, Elsevier, vol. 244(PB).
    3. Natarajan, Anisha & Krishnasamy, Vijayakumar & Singh, Munesh, 2022. "Occupancy detection and localization strategies for demand modulated appliance control in Internet of Things enabled home energy management system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    4. Kolny Beata, 2023. "Young Consumers Towards an Ecological Approach to Life in the Age of Smart Homes and Devices," Marketing of Scientific and Research Organizations, Sciendo, vol. 47(1), pages 105-126, March.
    5. Liu, Yinyan & Ma, Jin & Xing, Xinjie & Liu, Xinglu & Wang, Wei, 2022. "A home energy management system incorporating data-driven uncertainty-aware user preference," Applied Energy, Elsevier, vol. 326(C).
    6. Benakopoulos, Theofanis & Vergo, William & Tunzi, Michele & Salenbien, Robbe & Kolarik, Jakub & Svendsen, Svend, 2022. "Energy and cost savings with continuous low temperature heating versus intermittent heating of an office building with district heating," Energy, Elsevier, vol. 252(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shady Attia, 2020. "Spatial and Behavioral Thermal Adaptation in Net Zero Energy Buildings: An Exploratory Investigation," Sustainability, MDPI, vol. 12(19), pages 1-15, September.
    2. Kusiak, Andrew & Xu, Guanglin & Tang, Fan, 2011. "Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm," Energy, Elsevier, vol. 36(10), pages 5935-5943.
    3. Wong, S.L. & Wan, Kevin K.W. & Lam, Tony N.T., 2010. "Artificial neural networks for energy analysis of office buildings with daylighting," Applied Energy, Elsevier, vol. 87(2), pages 551-557, February.
    4. Lee, Junghun & Kim, Jeonggook & Song, Doosam & Kim, Jonghun & Jang, Cheolyong, 2017. "Impact of external insulation and internal thermal density upon energy consumption of buildings in a temperate climate with four distinct seasons," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 1081-1088.
    5. Goopyo Hong & Byungseon Sean Kim, 2018. "Development of a Data-Driven Predictive Model of Supply Air Temperature in an Air-Handling Unit for Conserving Energy," Energies, MDPI, vol. 11(2), pages 1-16, February.
    6. Cui, Hongzhi & Tang, Waiching & Qin, Qinghua & Xing, Feng & Liao, Wenyu & Wen, Haibo, 2017. "Development of structural-functional integrated energy storage concrete with innovative macro-encapsulated PCM by hollow steel ball," Applied Energy, Elsevier, vol. 185(P1), pages 107-118.
    7. Poggi, Francesca & Amado, Miguel, 2024. "The spatial dimension of energy consumption in cities," Energy Policy, Elsevier, vol. 187(C).
    8. Coma Bassas, Ester & Patterson, Joanne & Jones, Phillip, 2020. "A review of the evolution of green residential architecture," Renewable and Sustainable Energy Reviews, Elsevier, vol. 125(C).
    9. Ghoroghi, Ali & Petri, Ioan & Rezgui, Yacine & Alzahrani, Ateyah, 2023. "A deep learning approach to predict and optimise energy in fish processing industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 186(C).
    10. K A H Kobbacy & S Vadera & M H Rasmy, 2007. "AI and OR in management of operations: history and trends," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(1), pages 10-28, January.
    11. Yi Yuan & Yingjie Li & Jianli Zhao, 2018. "Development on Thermochemical Energy Storage Based on CaO-Based Materials: A Review," Sustainability, MDPI, vol. 10(8), pages 1-24, July.
    12. Dafermos, Yannis & Nikolaidi, Maria & Galanis, Giorgos, 2018. "Climate Change, Financial Stability and Monetary Policy," Ecological Economics, Elsevier, vol. 152(C), pages 219-234.
    13. Michele La Noce & Alessandro Lo Faro & Gaetano Sciuto, 2021. "Clay-Based Products Sustainable Development: Some Applications," Sustainability, MDPI, vol. 13(3), pages 1-26, January.
    14. Jomehzadeh, Fatemeh & Nejat, Payam & Calautit, John Kaiser & Yusof, Mohd Badruddin Mohd & Zaki, Sheikh Ahmad & Hughes, Ben Richard & Yazid, Muhammad Noor Afiq Witri Muhammad, 2017. "A review on windcatcher for passive cooling and natural ventilation in buildings, Part 1: Indoor air quality and thermal comfort assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 736-756.
    15. Michailidis, Iakovos T. & Schild, Thomas & Sangi, Roozbeh & Michailidis, Panagiotis & Korkas, Christos & Fütterer, Johannes & Müller, Dirk & Kosmatopoulos, Elias B., 2018. "Energy-efficient HVAC management using cooperative, self-trained, control agents: A real-life German building case study," Applied Energy, Elsevier, vol. 211(C), pages 113-125.
    16. Warner, Kevin J. & Jones, Glenn A., 2017. "A population-induced renewable energy timeline in nine world regions," Energy Policy, Elsevier, vol. 101(C), pages 65-76.
    17. Adrian Pitts, 2017. "Passive House and Low Energy Buildings: Barriers and Opportunities for Future Development within UK Practice," Sustainability, MDPI, vol. 9(2), pages 1-26, February.
    18. Kusiak, Andrew & Xu, Guanglin, 2012. "Modeling and optimization of HVAC systems using a dynamic neural network," Energy, Elsevier, vol. 42(1), pages 241-250.
    19. Cuong, Dinh Viet & Matsagar, Babasaheb M. & Lee, Mengshan & Hossain, Md. Shahriar A. & Yamauchi, Yusuke & Vithanage, Meththika & Sarkar, Binoy & Ok, Yong Sik & Wu, Kevin C.-W. & Hou, Chia-Hung, 2021. "A critical review on biochar-based engineered hierarchical porous carbon for capacitive charge storage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    20. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:283:y:2021:i:c:s0306261920316421. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.