also used manually-generated thermal models [@dong2014real; @Oldewurtel201215; @goyal2013occupancy] even though model creation is tedious and time-consuming and therefore expensive. Eager to demonstrate excellent performance, researchers have favored systems with complex topologies and numerous adjustments that yield “one-off” engineering efforts without a clear path to large-scale adoption. The system outlined in [@dong2014real], for instance, uses $\textrm{CO}_{2}$, sound, and light sensors that require carefully set detection thresholds for each room, plus an on-board weather forecasting algorithm in lieu of forecasts already available. We aim, instead, to make occupancy-predicting control accessible to a broader audience by presenting a simple but effective algorithm with a straightforward implementation. For example, we use an automated BIM translation facility outlined in a previous paper [@Greenberg201344], and the core algorithm is industry-standard [MPC]{} with occupancy weighting in the cost function. Each of the very few adjustments serves a clearly-defined purpose, and we have outlined each component’s operation with the practitioner in mind.

Second, recent research has paid little attention to the commissioning and maintenance of occupancy prediction algorithms; model training, if mentioned at all, has been a secondary consideration assumed to by done one time by someone skilled in the art [@page2008generalised; @dong2014real; @erickson2011observe]. Although most training algorithms 