Data-driven optimization of building layouts for energy efficiency

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 layouts of buildings. The optimization makes use of both a clustering-based approach and a genetic algorithm, and it aims 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 naive 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 151 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. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

Occupant behavioral dynamics play a key role in building energy performance, with layout optimization using clustering and genetic algorithms showing potential for reducing energy consumption. High diversity in occupant schedules is found to positively correlate with energy consumption of highly controllable lighting systems. The study demonstrates the benefits of utilizing low-cost dynamic design in building layouts to reduce energy usage and reach sustainable energy goals in the built environment.