  For effective integration of building operations into the evolving demand
response programs of the power grid, real-time decisions concerning the use of
building appliances for grid services must excel on multiple criteria, ranging
from the added value to occupants' comfort to the quality of the grid services.
In this paper, we present a data-driven decision-support framework to
dynamically rank load control alternatives in a commercial building, addressing
the needs of multiple decision criteria (e.g. occupant comfort, grid service
quality) under uncertainties in occupancy patterns. We adopt a stochastic
multi-criteria decision algorithm recently applied to prioritize residential
on/off loads, and extend it to i) complex load control decisions (e.g. dimming
of lights, changing zone temperature set-points) in a commercial building; and
ii) systematic integration of zonal occupancy patterns to accurately identify
short-term grid service opportunities. We evaluate the performance of the
framework for curtailment of air-conditioning, lighting, and plug-loads in a
multi-zone office building for a range of design choices. With the help of a
prototype system that integrates an interactive \textit{Data Analytics and
Visualization} frontend, we demonstrate a way for the building operators to
monitor the flexibility in energy consumption and to develop trust in the
decision recommendations by interpreting the rationale behind the ranking.
