Application of occupant behavior prediction model on residential big data analysis

Abstract

Occupant behavior is multifaceted, and a systematic approach is required to understand occupant behavior comprehensively. This research aims to define a structure of the relationship between energy consumption, building technology, and occupant behavior, using the Occupant Behavior Prediction Model. The model can predict and explain occupant energy usage-related activities. A machine learning approach is used to develop the model, and datasets from the American Time Use Survey (ATUS) are used to verify the model. The results show that the energy use activities with higher predictive performances are more stable and habitual compared to the ones with lower predictive performances. The prediction accuracy achieved by this model for these habitual activities reached as high as 99%. The findings imply that the building systems and control strategies need to be adjusted to accommodate habitual energy use behaviors, rather than changing the behaviors. In addition, educational interventions seem more effective on the less habitual behaviors, which often change.