Data-driven optimization of building layouts for energy efficiency

TL;DR

This paper presents data-driven methods to optimize building layouts for energy efficiency by modeling occupant behavior and using algorithms to reduce lighting energy consumption in office spaces.

Contribution

It introduces a novel approach combining occupant behavior modeling with clustering and genetic algorithms for layout optimization to lower energy use.

Findings

Occupant behavior diversity correlates with lighting energy consumption.

Layout optimization methods achieved approximately 5% energy savings.

Data-driven simulation validates the effectiveness of the proposed optimization techniques.

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.