|Title||Data-Driven Optimization of Building Layouts for Energy Efficiency|
|Publication Type||Journal Article|
|Year of Publication||2021|
|Authors||Sonta, A, Dougherty, T, Jain, R|
|Journal||Energy & Buildings|
|Issue||238 (2021) 110815|
|Keywords||Data-driven simulation, Design optimization, Energy Efficiency, Machine learning, Occupant dynamics|
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 (ie, 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 naïve 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.