Individual Data Sparsity in Smart Thermostat Big Data: Impacts on Modeling Thermostat Use Behavior Dynamics

Conference · Wed Nov 20 04:00:00 EST 2024

OSTI ID:2998506

Fannon, David ^[1]; Kane, Michael; Sharma, Kunind ^[1]; Vahdat, Manaz ^[1]

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1. Northeastern Univ., Boston, MA (United States)

This study explores the impacts of the sparsity of individual thermostat interaction data on modeling thermostat use behavior dynamics using a dataset of over 100,000 smart thermostats. In developing a data-driven model of Thermal Frustration Theory (TFT), we investigate the challenges and trade-offs in clustering occupant data to enhance predictive accuracy. Our findings reveal that a single, aggregated model fails to capture the diversity of occupant behaviors, resulting in extremely poor prediction performance. Conversely, excessive clustering exacerbates data sparsity, undermining model reliability. By identifying an optimal clustering strategy, we achieve a balance that significantly improves the prediction of manual setpoint changes during demand response (DR) events, enhancing energy management and occupant comfort

• Research Organization: Northeastern University; National Energy Technology Laboratory
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office
• DOE Contract Number: EE0009154
• OSTI ID: 2998506
• Country of Publication: United States
• Language: English