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Open AccessArticle

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

Department of Mechanical & Aerospace Engineering, University of Dayton, Dayton, OH 45469-0238, USA
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Sustainability 2020, 12(17), 7110; https://doi.org/10.3390/su12177110
Received: 23 July 2020 / Revised: 20 August 2020 / Accepted: 28 August 2020 / Published: 31 August 2020
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. View Full-Text
Keywords: smart WiFi thermostats; back propagation neural network; long-short term memory; encoder-eecoder LSTM; demand management; energy savings smart WiFi thermostats; back propagation neural network; long-short term memory; encoder-eecoder LSTM; demand management; energy savings
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MDPI and ACS Style

Huang, K.; Hallinan, K.P.; Lou, R.; Alanezi, A.; Alshatshati, S.; Sun, Q. Self-Learning Algorithm to Predict Indoor Temperature and Cooling Demand from Smart WiFi Thermostat in a Residential Building. Sustainability 2020, 12, 7110. https://doi.org/10.3390/su12177110

AMA Style

Huang K, Hallinan KP, Lou R, Alanezi A, Alshatshati S, Sun Q. Self-Learning Algorithm to Predict Indoor Temperature and Cooling Demand from Smart WiFi Thermostat in a Residential Building. Sustainability. 2020; 12(17):7110. https://doi.org/10.3390/su12177110

Chicago/Turabian Style

Huang, Kefan; Hallinan, Kevin P.; Lou, Robert; Alanezi, Abdulrahman; Alshatshati, Salahaldin; Sun, Qiancheng. 2020. "Self-Learning Algorithm to Predict Indoor Temperature and Cooling Demand from Smart WiFi Thermostat in a Residential Building" Sustainability 12, no. 17: 7110. https://doi.org/10.3390/su12177110

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