Data-driven optimization of building layouts for energy efficiency

• URL: http://arxiv.org/abs/2007.12796v1
• Date: Fri, 24 Jul 2020 22:58:16 GMT
• Title: Data-driven optimization of building layouts for energy efficiency
• Authors: Andrew Sonta, Thomas R. Dougherty, Rishee K. Jain
• Abstract summary: We introduce methods for relating lighting zone energy to zone-level occupant dynamics, simulating energy consumption of a lighting system based on this relationship. We find in a case study that nonhomogeneous behavior 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 produce layouts that reduce energy consumption by roughly 5% compared to the existing layout of a real office space comprised of 165 occupants.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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