WELCOME TO SHBE RCN

The Predictive Modeling Network for Sustainable Human-Building Ecosystem (SHBE) is a Research Coordination Network (RCN), funded by National Science Foundation (NSF). It aims to develop a collaborative research platform centered on overcoming bottlenecks in engineering, software and social/economic sciences that impede wider application of sustainable building technology. The network activities will focus on defining an innovative, new interdisciplinary area, “Sustainable Human-Building Ecosystem (SHBE)” that integrates human behavioral science, social and economic sciences in tandem with sciences of building design, engineering, and metrology for data validation of building energy consumption and occupant comforts and overall wellbeing.

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WHO WE ARE

We are a multi-disciplinary group of researchers, including emerging researchers (students), with the background in architecture, engineering, computer science, and social science, with strong, common interests to collaborate and explore emerging research topics that impact human and society in the context of a human-building ecosystem for the sustainable future. The network is dynamic, and participation is invited.

Research Themes

Physical systems

This theme includes architectural design and building equipment and systems for thermal and audio/visual comforts, including structural and thermal envelopes…

Human behaviors

This theme centers on human interaction with buildings, from both design and operation viewpoints, towards sustainability and acceptability. It requires…

Social/Policy impact

The theme focuses on the understanding of diffusion of technology and diffusion of the ecosystem concept to policy making process and their…

Life cycle/economics

This theme focuses on two important aspects: (a) Understanding the consequences of incorporating significant human behavior factors in life cycle assessment…

Model integration & validation

The ultimate goal of this network effort is to explore the new modeling methodology that enables the effective integration of interdisciplinary data models and rigorous validation…