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Self-Learning Algorithm to Predict Indoor Temperature and Cooling Demand from Smart WiFi Thermostat in a Residential Building

Author

Listed:
  • Kefan Huang

    (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)

  • Robert Lou

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

  • Abdulrahman Alanezi

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

  • Salahaldin Alshatshati

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

  • Qiancheng Sun

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

Abstract

Smart WiFi thermostats have moved well beyond the function they were originally designed for; namely, controlling heating and cooling comfort in buildings. They are now also learning from occupant behaviors and permit occupants to control their comfort remotely. This research seeks to go beyond this state of the art by utilizing smart WiFi thermostat data in residences to develop dynamic predictive models for room temperature and cooling/heating demand. These models can then be used to estimate the energy savings from new thermostat temperature schedules and estimate peak load reduction achievable from maintaining a residence in a minimum thermal comfort condition. Back Propagation Neural Network (BPNN), Long-Short Term Memory (LSTM), and Encoder-Decoder LSTM dynamic models are explored. Results demonstrate that LSTM outperforms BPNN and Encoder-Decoder LSTM approach, yielding and a MAE error of 0.5 °C, equal to the resolution error of the measured temperature. Additionally, the models developed are shown to be highly accurate in predicting savings from aggressive thermostat set point schedules, yielding deep reduction of up to 14.3% for heating and cooling, as well as significant energy reduction from curtailed thermal comfort in response to a high demand event.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:17:p:7110-:d:406651
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    References listed on IDEAS

    as
    1. 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.
    2. Fan, Cheng & Xiao, Fu & Zhao, Yang, 2017. "A short-term building cooling load prediction method using deep learning algorithms," Applied Energy, Elsevier, vol. 195(C), pages 222-233.
    3. Nivine Attoue & Isam Shahrour & Rafic Younes, 2018. "Smart Building: Use of the Artificial Neural Network Approach for Indoor Temperature Forecasting," Energies, MDPI, vol. 11(2), pages 1-12, February.
    4. Janet L. Reyna & Mikhail V. Chester, 2017. "Energy efficiency to reduce residential electricity and natural gas use under climate change," Nature Communications, Nature, vol. 8(1), pages 1-12, August.
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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. 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.
    3. 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.
    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. Song, Jiancai & Bian, Tianxiang & Xue, Guixiang & Wang, Hanyu & Shen, Xingliang & Wu, Xiangdong, 2023. "Short-term forecasting model for residential indoor temperature in DHS based on sequence generative adversarial network," Applied Energy, Elsevier, vol. 348(C).

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