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As residential thermostats have become increasingly capable of sensing occupancy, reliably predicting future occupancy over short-term control horizons becomes a key mechanism for improving the efficiency of heating, ventilation, and air conditioning (HVAC) systems. Past research has established successful conditional dependencies in modeling and simulating future occupancy states for residential applications based on manually collected or sensed data. These studies often are small-scale tests with limited use of commercially available data. Using a large dataset representing the relatively new class of devices called connected thermostats, two probabilistic methods were identified and implemented in hopes of identifying candidates for future control and simulation applications. Logistic regression and a Markov model were used to conduct inference on a defined model of motion detection. Testing was done on actual device data from 1000 real homes. Both methods were found to have average accuracies above 80% in next timestep prediction. The Markov model performs better over a longer horizon (of up to 3 hours). In classification error types, the Markov model outperforms the logistic model both in terms of false positive and false negative rates. The results of the logistic regression are shown to improve with some simple encoding strategies.

Short-Horizon Probabilistic Models For Whole Home Motion Prediction Using Connected Thermostat Data