Segmentation Analysis in Human Centric Cyber-Physical Systems using Graphical Lasso

TL;DR

This paper introduces a Graphical Lasso-based segmentation method for human-centric cyber-physical systems, enabling better understanding of occupant energy behaviors for improved sustainability and personalized engagement.

Contribution

It proposes a novel application of Graphical Lasso for occupant energy behavior segmentation within a human-centric cyber-physical framework.

Findings

Identified distinct energy usage behavior clusters.

Features influencing human decision-making are made explainable.

Demonstrated the effectiveness of Graphical Lasso in behavioral segmentation.

Abstract

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.