not account for thermal lag or expected occupancy and therefore runs the least. Not surprisingly, its comfort performance upon occupant arrival is very poor, with large leading spikes on the discomfort trace in Figure \[fig:Simulation-results\]c and frequent calls for maximum output power in Figure \[fig:Simulation-results\]d. The scheduled controller leaves plenty of margin around the typical occupancy envelope and consequently yields excellent comfort at the expense of energy efficiency. The comfort performance of occupancy predicting MPC lies between these two methods, with peak discomfort slightly worse than scheduled control but without the severe deviations of triggered control. Table \[fig:resultTable\] shows up to 19% energy savings compared to the scheduled controller and significantly lower peak discomfort than the occupancy-triggered controller.

Perhaps more interesting than the discomfort peak is its distribution. Figure \[fig:histogram\] shows how many times various occupancy-weighted discomfort levels occur under each control method. It comes as little surprise that the scheduled controller maintains discomfort within $2^{\circ}\textrm{C}$ at all times. (Clearly, though, an out-of-date schedule would not perform this well, so this is a rather optimistic profile of scheduled control.) The occupancy-predicting controller maintains discomfort less than $2^{\circ}\textrm{C}$ more than 94% of the time with relatively mild outliers. The occupancy-triggered controller 