  HVAC systems account for a significant portion of building energy use.
Nighttime setback scheduling is an energy conservation measure where cooling
and heating setpoints are increased and decreased respectively during
unoccupied periods with the goal of obtaining energy savings. However,
knowledge of a building's real occupancy is required to maximize the success of
this measure. In addition, there is the need for a scalable way to estimate
energy savings potential from energy conservation measures that is not limited
by building specific parameters and experimental or simulation modeling
investments. Here, we propose MARTINI, a sMARt meTer drIveN estImation of
occupant-derived HVAC schedules and energy savings that leverages the ubiquity
of energy smart meters and WiFi infrastructure in commercial buildings. We
estimate the schedules by clustering WiFi-derived occupancy profiles and,
energy savings by shifting ramp-up and setback times observed in
typical/measured load profiles obtained by clustering smart meter energy
profiles. Our case-study results with five buildings over seven months show an
average of 8.1%-10.8% (summer) and 0.2%-5.9% (fall) chilled water energy
savings when HVAC system operation is aligned with occupancy. We validate our
method with results from building energy performance simulation (BEPS) and find
that estimated average savings of MARTINI are within 0.9%-2.4% of the BEPS
predictions. In the absence of occupancy information, we can still estimate
potential savings from increasing ramp-up time and decreasing setback start
time. In 51 academic buildings, we find savings potentials between 1%-5%.
