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

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  • 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
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    References listed on IDEAS

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    1. Jones, Glenn A. & Warner, Kevin J., 2016. "The 21st century population-energy-climate nexus," Energy Policy, Elsevier, vol. 93(C), pages 206-212.
    2. 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.
    3. 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.
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    Cited by:

    1. Artur Strzelecki & Beata Kolny & Michał Kucia, 2024. "Smart Homes as Catalysts for Sustainable Consumption: A Digital Economy Perspective," Sustainability, MDPI, vol. 16(11), pages 1-26, May.
    2. Fabio Gualandri & Aleksandra Kuzior, 2023. "Home Energy Management Systems Adoption Scenarios: The Case of Italy," Energies, MDPI, vol. 16(13), pages 1-20, June.
    3. 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).
    4. 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).
    5. Kuang, Biao & Shi, Yangming & Hu, Yuqing & Zeng, Zhaoyun & Chen, Jianli, 2024. "Household energy resilience in extreme weather events: An investigation of energy service importance, HVAC usage behaviors, and willingness to pay," Applied Energy, Elsevier, vol. 363(C).
    6. 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.
    7. 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).
    8. 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).

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