  One of the primary driving factors in building energy performance is occupant
behavioral dynamics. As a result, the layout of building occupant workstations
is likely to influence energy consumption. In this paper, we introduce methods
for relating lighting zone energy to zone-level occupant dynamics, simulating
energy consumption of a lighting system based on this relationship, and
optimizing the layout of buildings through the use of both a clustering-based
approach and a genetic algorithm in order to reduce energy consumption. We find
in a case study that nonhomogeneous behavior (i.e., high diversity) among
occupant schedules positively correlates with the energy consumption of a
highly controllable lighting system. We additionally find through data-driven
simulation that the na\"ive clustering-based optimization and the genetic
algorithm (which makes use of the energy simulation engine) produce layouts
that reduce energy consumption by roughly 5% compared to the existing layout of
a real office space comprised of 165 occupants. Overall, this study
demonstrates the merits of utilizing low-cost dynamic design of existing
building layouts as a means to reduce energy usage. Our work provides an
additional path to reach our sustainable energy goals in the built environment
through new non-capital-intensive interventions.
