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Smart Wifi Thermostat-Enabled Thermal Comfort Control in Residences

Author

Listed:
  • Robert Lou

    (Department of Mechanical & Aerospace Engineering, University of Dayton, Dayton, OH 45469-0238, USA)

  • Kevin P. Hallinan

    (Department of Mechanical & Aerospace Engineering, University of Dayton, Dayton, OH 45469-0238, USA)

  • Kefan Huang

    (Department of Mechanical & Aerospace Engineering, University of Dayton, Dayton, OH 45469-0238, USA)

  • Timothy Reissman

    (Department of Mechanical & Aerospace Engineering, University of Dayton, Dayton, OH 45469-0238, USA)

Abstract

The present research leverages prior works to automatically estimate wall and ceiling R-values using a combination of a smart WiFi thermostat, building geometry, and historical energy consumption data to improve the calculation of the mean radiant temperature (MRT), which is integral to the determination of thermal comfort in buildings. To assess the potential of this approach for realizing energy savings in any residence, machine learning predictive models of indoor temperature and humidity, based upon a nonlinear autoregressive exogenous model (NARX), were developed. The developed models were used to calculate the temperature and humidity set-points needed to achieve minimum thermal comfort at all times. The initial results showed cooling energy savings in excess of 83% and 95%, respectively, for high- and low-efficiency residences. The significance of this research is that thermal comfort control can be employed to realize significant heating, ventilation, and air conditioning (HVAC) savings using readily available data and systems.

Suggested Citation

  • Robert Lou & Kevin P. Hallinan & Kefan Huang & Timothy Reissman, 2020. "Smart Wifi Thermostat-Enabled Thermal Comfort Control in Residences," Sustainability, MDPI, vol. 12(5), pages 1-15, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:5:p:1919-:d:327846
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    Citations

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    Cited by:

    1. Abdulelah D. Alhamayani & Qiancheng Sun & Kevin P. Hallinan, 2022. "An Improved Method to Estimate Savings from Thermal Comfort Control in Residences from Smart Wi-Fi Thermostat Data," Clean Technol., MDPI, vol. 4(2), pages 1-12, May.
    2. Pedro Macieira & Luis Gomes & Zita Vale, 2021. "Energy Management Model for HVAC Control Supported by Reinforcement Learning," Energies, MDPI, vol. 14(24), pages 1-14, December.
    3. Michela Borghetti & Edoardo Cantù & Emilio Sardini & Mauro Serpelloni, 2020. "Future Sensors for Smart Objects by Printing Technologies in Industry 4.0 Scenario," Energies, MDPI, vol. 13(22), pages 1-15, November.
    4. Abdulrahman Alanezi & Kevin P. Hallinan & Kefan Huang, 2021. "Automated Residential Energy Audits Using a Smart WiFi Thermostat-Enabled Data Mining Approach," Energies, MDPI, vol. 14(9), pages 1-23, April.
    5. Abdulrahman Alanezi & Kevin P. Hallinan & Rodwan Elhashmi, 2021. "Using Smart-WiFi Thermostat Data to Improve Prediction of Residential Energy Consumption and Estimation of Savings," Energies, MDPI, vol. 14(1), pages 1-16, January.
    6. Stefano Villa & Claudio Sassanelli, 2020. "The Data-Driven Multi-Step Approach for Dynamic Estimation of Buildings’ Interior Temperature," Energies, MDPI, vol. 13(24), pages 1-23, December.
    7. Abdulelah D. Alhamayani & Qiancheng Sun & Kevin P. Hallinan, 2021. "Estimating Smart Wi-Fi Thermostat-Enabled Thermal Comfort Control Savings for Any Residence," Clean Technol., MDPI, vol. 3(4), pages 1-18, October.
    8. V. S. K. V. Harish & Arun Kumar & Tabish Alam & Paolo Blecich, 2021. "Assessment of State-Space Building Energy System Models in Terms of Stability and Controllability," Sustainability, MDPI, vol. 13(21), pages 1-26, October.
    9. Sarran, Lucile & Smith, Kevin M. & Hviid, Christian A. & Rode, Carsten, 2022. "Grey-box modelling and virtual sensors enabling continuous commissioning of hydronic floor heating," Energy, Elsevier, vol. 261(PB).
    10. Marek Borowski & Klaudia Zwolińska & Marcin Czerwiński, 2022. "An Experimental Study of Thermal Comfort and Indoor Air Quality—A Case Study of a Hotel Building," Energies, MDPI, vol. 15(6), pages 1-18, March.
    11. Kefan Huang & Kevin P. Hallinan & Robert Lou & Abdulrahman Alanezi & Salahaldin Alshatshati & Qiancheng Sun, 2020. "Self-Learning Algorithm to Predict Indoor Temperature and Cooling Demand from Smart WiFi Thermostat in a Residential Building," Sustainability, MDPI, vol. 12(17), pages 1-14, August.
    12. Halhoul Merabet, Ghezlane & Essaaidi, Mohamed & Ben Haddou, Mohamed & Qolomany, Basheer & Qadir, Junaid & Anan, Muhammad & Al-Fuqaha, Ala & Abid, Mohamed Riduan & Benhaddou, Driss, 2021. "Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).

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