Andrew J. Sonta

TL;DR: A critical review of existing studies applying computer-based modeling and simulation to guide occupant-centric building design, covering large-scale international data collection efforts to move from generic assumptions about occupant behavior to specific/localized knowledge, and improved metrics of measuring building performance.

TL;DR: A methodology for classifying occupant activity patterns from plug load sensor data at the desk level using a common unsupervised learning algorithm—the Gaussian mixture model—and it incorporates explicit knowledge about occupant presence and absence in order to preserve adaptability and effectiveness.

TL;DR: 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 are introduced.

TL;DR: This initial work presents a methodology for using distributed plug load energy consumption sensors to infer the social/organizational network of occupants, and introduces how statistical methods---in particular, the graphical lasso and the influence model---can be used to learn network structure from time-series activity data.