Estimating Smart Wi-Fi Thermostat-Enabled Thermal Comfort Control Savings for Any Residence

Nowadays, most indoor cooling control strategies are based solely on the dry-bulb temperature, which is not close to a guarantee of thermal comfort of occupants. Prior research has shown cooling energy savings from use of a thermal comfort control methodology ranging from 10 to 85%. The present research advances prior research to enable thermal comfort control in residential buildings using a smart Wi-Fi thermostat. “Fanger’s Predicted Mean Vote model” is used to define thermal comfort. A machine learning model leveraging historical smart Wi-Fi thermostat data and outdoor temperature is trained to predict indoor temperature. A Long Short-Term-Memory neural network algorithm is employed for this purpose. The model considers solar heat input estimations to a residence as input features. The results show that this approach yields a substantially improved ability to accurately model and predict indoor temperature. Secondly, it enables a more accurate estimation of potential savings from thermal comfort control. Cooling energy savings ranging from 33 to 47% are estimated based upon real data for variable energy effectiveness and solar exposed residences.

[1]  Kevin P. Hallinan,et al.  Self-Learning Algorithm to Predict Indoor Temperature and Cooling Demand from Smart WiFi Thermostat in a Residential Building , 2020 .

[2]  Hui Zhang,et al.  Modeling the comfort effects of short-wave solar radiation indoors , 2015 .

[3]  Jong Lee,et al.  Thermal Comfort, Energy and Cost Impacts of PMV Control Considering Individual Metabolic Rate Variations in Residential Building , 2018, Energies.

[4]  Burcin Becerik-Gerber,et al.  Energy trade off analysis of optimized daily temperature setpoints , 2018, Journal of Building Engineering.

[5]  Peng Yang,et al.  Electric Vehicle Tour Planning Considering Range Anxiety , 2020, Sustainability.

[6]  Adel Gastli,et al.  Reinforcement Learning-Based School Energy Management System , 2020, Energies.

[7]  O. T. Masoso,et al.  The dark side of occupants’ behaviour on building energy use , 2010 .

[8]  Liu Yang,et al.  Thermal comfort and building energy consumption implications - A review , 2014 .

[9]  Frits de Nijs,et al.  Reinforcement learning for whole-building HVAC control and demand response , 2020, Energy and AI.

[10]  Taeyeon Kim,et al.  Development of Thermal Comfort-Based Controller and Potential Reduction of the Cooling Energy Consumption of a Residential Building in Kuwait , 2019, Energies.

[11]  Kevin P. Hallinan,et al.  Automated Residential Energy Audits Using a Smart WiFi Thermostat-Enabled Data Mining Approach , 2021 .

[12]  Robert J. Brecha,et al.  Large scale residential energy efficiency prioritization enabled by machine learning , 2019, Energy Efficiency.

[13]  Francesca Romana d’Ambrosio Alfano,et al.  Fifty Years of PMV Model: Reliability, Implementation and Design of Software for Its Calculation , 2019 .

[14]  Yanfeng Gong,et al.  The Smart Thermostat of HVAC Systems Based on PMV-PPD Model for Energy Efficiency and Demand Response , 2018, 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2).

[15]  Timothy Reissman,et al.  Smart Wifi Thermostat-Enabled Thermal Comfort Control in Residences , 2020, Sustainability.

[16]  Qingyan Chen,et al.  Artificial neural network models using thermal sensations and occupants’ behavior for predicting thermal comfort , 2018, Energy and Buildings.

[17]  Burcin Becerik-Gerber,et al.  Quantifying the influence of temperature setpoints, building and system features on energy consumption , 2015, 2015 Winter Simulation Conference (WSC).

[18]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[19]  Hong Qin,et al.  Redundant features removal for unsupervised spectral feature selection algorithms: an empirical study based on nonparametric sparse feature graph , 2018, International Journal of Data Science and Analytics.

[20]  Bijan Samali,et al.  A review of different strategies for HVAC energy saving , 2014 .

[21]  Paul Raftery,et al.  Time-averaged ventilation for optimized control of variable-air-volume systems , 2017 .