Automation in Building Occupant Profile Development: A Machine Learning- and Persona-Enabled Approach

The user persona is a communication tool for designers to generate a mental model that describes the archetype of users. Developing building occupant personas has proven to be an effective method for human-centered smart building design, considering occupant comfort, behavior, and energy consumption. The current approaches to developing building occupant personas face a major obstacle of manual data processing and analysis. This study proposes a machine learning-based approach for occupant characteristics' classification and prediction with a view toward partially automating the building occupant persona generation process. We investigate the 2015 Residential Energy Consumption Dataset using six machine learning techniques for predicting 16 occupant characteristics, such as age, education, and thermal comfort. The models achieved moderate accuracy in predicting most of the occupant characteristics and significantly higher accuracy (over 90%) for attributes including the number of occupants in the household, their age group, and preferred usage of primary cooling equipment. The results of the study show the feasibility of using machine learning techniques for occupant characteristic prediction and automating the development of building occupant persona to minimize human effort.