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.
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.
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