Segmentation Analysis in Human Centric Cyber-Physical Systems using Graphical Lasso
This work addresses energy efficiency in smart buildings by enabling building managers to interact with occupants and design incentives, though it appears incremental in applying an existing statistical method to a new domain.
The paper tackles the problem of understanding occupant energy usage behavior in human-centric cyber-physical systems by proposing a segmentation analysis using Graphical Lasso, which identifies characteristic clusters of energy usage behaviors and makes human decision-making factors explainable.
A generalized gamification framework is introduced as a form of smart infrastructure with potential to improve sustainability and energy efficiency by leveraging humans-in-the-loop strategy. The proposed framework enables a Human-Centric Cyber-Physical System using an interface to allow building managers to interact with occupants. The interface is designed for occupant engagement-integration supporting learning of their preferences over resources in addition to understanding how preferences change as a function of external stimuli such as physical control, time or incentives. Towards intelligent and autonomous incentive design, a noble statistical learning algorithm performing occupants energy usage behavior segmentation is proposed. We apply the proposed algorithm, Graphical Lasso, on energy resource usage data by the occupants to obtain feature correlations--dependencies. Segmentation analysis results in characteristic clusters demonstrating different energy usage behaviors. The features--factors characterizing human decision-making are made explainable.