  The user persona is a communication tool for designers to generate a mental
model that describes the archetype of users. Developing building occupant
personas is proven to be an effective method for human-centered smart building
design, which considers occupant comfort, behavior, and energy consumption.
Optimization of building energy consumption also requires a deep understanding
of occupants' preferences and behaviors. The current approaches to developing
building occupant personas face a major obstruction of manual data processing
and analysis. In this study, we propose and evaluate a machine learning-based
semi-automated approach to generate building occupant personas. We investigate
the 2015 Residential Energy Consumption Dataset with five machine learning
techniques - Linear Discriminant Analysis, K Nearest Neighbors, Decision Tree
(Random Forest), Support Vector Machine, and AdaBoost classifier - for the
prediction of 16 occupant characteristics, such as age, education, and, thermal
comfort. The models achieve an average accuracy of 61% and accuracy over 90%
for attributes including the number of occupants in the household, their age
group, and preferred usage of heating or cooling equipment. The results of the
study show the feasibility of using machine learning techniques for the
development of building occupant persona to minimize human effort.
