A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Data-driven optimization of building layouts for energy efficiency
https://orcid.org/0000-0001-9021-0842
Graphical abstract Display Omitted
Highlights Building energy consumption is largely driven by diversity in occupant behavior. Machine learning can be used to predict lighting energy use with occupancy data. Building layouts are optimized with a naïve clustering method and genetic algorithm. Layout optimization can yield 5–6% reduction in lighting energy use.
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 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 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 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.
Data-driven optimization of building layouts for energy efficiency
https://orcid.org/0000-0001-9021-0842
Graphical abstract Display Omitted
Highlights Building energy consumption is largely driven by diversity in occupant behavior. Machine learning can be used to predict lighting energy use with occupancy data. Building layouts are optimized with a naïve clustering method and genetic algorithm. Layout optimization can yield 5–6% reduction in lighting energy use.
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 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 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 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.
Data-driven optimization of building layouts for energy efficiency
https://orcid.org/0000-0001-9021-0842
Graphical abstract Display Omitted
Highlights Building energy consumption is largely driven by diversity in occupant behavior. Machine learning can be used to predict lighting energy use with occupancy data. Building layouts are optimized with a naïve clustering method and genetic algorithm. Layout optimization can yield 5–6% reduction in lighting energy use.
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 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 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 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.
Data-driven optimization of building layouts for energy efficiency
Sonta, Andrew (author) / Dougherty, Thomas R. (author) / Jain, Rishee K. (author)
Energy and Buildings ; 238
2021-02-06
Article (Journal)
Electronic Resource
English
Urban Design Synthesis for Building Layouts Based on Evolutionary Many-Criteria Optimization
| SAGE Publications | 2015