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Using Smart-WiFi Thermostat Data to Improve Prediction of Residential Energy Consumption and Estimation of Savings

Author

Listed:
  • Abdulrahman Alanezi

    (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)

  • Rodwan Elhashmi

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

Abstract

Energy savings based upon use of smart WiFi thermostats ranging from 10 to 15% have been documented, as new features such as geofencing have been added. Here, a new benefit of smart WiFi thermostats is identified and investigated; namely, as a tool to improve the estimation accuracy of residential energy consumption and, as a result, estimation of energy savings from energy system upgrades, when only monthly energy consumption is metered. This is made possible from the higher sampling frequency of smart WiFi thermostats. In this study, collected smart WiFi data are combined with outdoor temperature data and known residential geometrical and energy characteristics. Most importantly, unique power spectra are developed for over 100 individual residences from the measured thermostat indoor temperature in each and used as a predictor in the training of a singular machine learning models to predict consumption in any residence. The best model yielded a percentage mean absolute error (MAE) for monthly gas consumption ±8.6%. Applied to two residences to which attic insulation was added, the resolvable energy savings percentage is shown to be approximately 5% for any residence, representing an improvement in the ASHRAE recommended approach for estimating savings from whole-building energy consumption that is deemed incapable at best of resolving savings less than 10% of total consumption. The approach posited thus offers value to utility-wide energy savings measurement and verification.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:1:p:187-:d:473524
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Li, Qiong & Meng, Qinglin & Cai, Jiejin & Yoshino, Hiroshi & Mochida, Akashi, 2009. "Applying support vector machine to predict hourly cooling load in the building," Applied Energy, Elsevier, vol. 86(10), pages 2249-2256, October.
    4. 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.
    5. Zhao, Deyin & Zhong, Ming & Zhang, Xu & Su, Xing, 2016. "Energy consumption predicting model of VRV (Variable refrigerant volume) system in office buildings based on data mining," Energy, Elsevier, vol. 102(C), pages 660-668.
    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.
    7. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    8. Özmen, Ayşe & Yılmaz, Yavuz & Weber, Gerhard-Wilhelm, 2018. "Natural gas consumption forecast with MARS and CMARS models for residential users," Energy Economics, Elsevier, vol. 70(C), pages 357-381.
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    Cited by:

    1. Manuel Avila & Juana Isabel Méndez & Pedro Ponce & Therese Peffer & Alan Meier & Arturo Molina, 2021. "Energy Management System Based on a Gamified Application for Households," Energies, MDPI, vol. 14(12), pages 1-27, June.
    2. Gerard Mor & Jordi Cipriano & Eloi Gabaldon & Benedetto Grillone & Mariano Tur & Daniel Chemisana, 2021. "Data-Driven Virtual Replication of Thermostatically Controlled Domestic Heating Systems," Energies, MDPI, vol. 14(17), pages 1-25, September.

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