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Automated Residential Energy Audits Using a Smart WiFi Thermostat-Enabled Data Mining Approach

Author

Listed:
  • Abdulrahman Alanezi

    (Department of Mechanical and Aerospace Engineering/Renewable and Clean Energy, University of Dayton, Dayton, OH 45469, USA)

  • Kevin P. Hallinan

    (Department of Mechanical and Aerospace Engineering/Renewable and Clean Energy, University of Dayton, Dayton, OH 45469, USA)

  • Kefan Huang

    (Department of Mechanical and Aerospace Engineering/Renewable and Clean Energy, University of Dayton, Dayton, OH 45469, USA)

Abstract

Smart WiFi thermostats, when they first reached the market, were touted as a means for achieving substantial heating and cooling energy cost savings. These savings did not materialize until additional features, such as geofencing, were added. Today, average savings from these thermostats of 10–12% in heating and 15% in cooling for a single-family residence have been reported. This research aims to demonstrate additional potential benefit of these thermostats, namely as a potential instrument for conducting virtual energy audits on residences. In this study, archived smart WiFi thermostat measured temperature data in the form of a power spectrum, corresponding historical weather and energy consumption data, building geometry characteristics, and occupancy data were integrated in order to train a machine learning model to predict attic and wall R-Values, furnace efficiency, and air conditioning seasonal energy efficiency ratio (SEER), all of which were known for all residences in this study. The developed model was validated on residences not used for model development. Validation R-squared values of 0.9408, 0.9421, 0.9536, and 0.9053 for predicting attic and wall R-values, furnace efficiency, and AC SEER, respectively, were realized. This research demonstrates promise for low-cost data-based energy auditing of residences reliant upon smart WiFi thermostats.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:9:p:2500-:d:544516
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    References listed on IDEAS

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    1. 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.
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    6. 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.
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    1. 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.
    2. 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.
    3. Jennifer Hoody & Anya Galli Robertson & Sarah Richard & Claire Frankowski & Kevin Hallinan & Ciara Owens & Bob Pohl, 2021. "A Review of Behavioral Energy Reduction Programs and Implementation of a Pilot Peer-to-Peer Led Behavioral Energy Reduction Program for a Low-Income Neighborhood," Energies, MDPI, vol. 14(15), pages 1-27, July.

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