Estimating Occupancy States from Building Temperature Data Using Wavelet Analysis

Whole building energy models have found widespread use in estimating the energy consumption of building systems. Within these models, usage profiles are assumed when capturing the influence of processes such as occupancy, lighting, and equipment operation. Usage profiles are defined hourly, but are repetitive in the sense that their shapes are periodic, typically, from week to week. When evaluating a design, the use of periodic usage profiles is accepted since detailed knowledge of building operation is usually unknown. In the case of an existing building, however, this approach may not accurately capture building behavior and cause an error in prediction from the resulting mismatch in operation between the actual and modeled building. In this work, fluctuations in space occupation of a multi-use university building is studied. By decomposing building temperature data using wavelets, room occupancy states (i.e. the status of a room being occupied or vacant) are estimated. From this estimate, usage profiles are generated which better capture the actual behavior of the building. Predictions of energy usage from a model simulation which utilizes this implementation is compared to building utility data as well as a simulation where conventional usage profiles are assumed.