Project Details
[Return to Previous Page]Risk-Informed Smart Commissioning for Resilient Building Performance
Company: PSU Cocoziello Institute of Real Estate Innovation
Major(s):
Primary: CMPSC
Secondary: IE
Optional: EGEE
Non-Disclosure Agreement: NO
Intellectual Property: NO
Background: Commissioning is the process of testing and fine-tuning a building's mechanical and control systems so that they operate as intended. Smart commissioning goes one step further by using data automatically collected from sensors, meters, and building-automation systems to detect problems, evaluate performance, and recommend fixes in real time. Put differently, it applies data science and engineering analytics to make buildings "self-aware" and therefore "smarter". Project Goal: While smart commissioning has been consistently shown to improve building performance, energy efficiency, and occupant satisfaction, there remains a lack of a systematic framework that connects these proven operational and human-centric benefits to measurable reductions in real estate investment and insurance risk. This disconnect represents a significant market barrier to broader adoption of commissioning practices, as building owners and investors often struggle to translate performance gains into tangible financial or risk-based returns. This project proposes a risk-informed framework that links smart commissioning outcomes to quantifiable reductions in risk exposure and improvements in financial performance. By integrating building performance analytics, predictive diagnostics, and probabilistic risk modeling, the framework will provide a structured way to evaluate how enhanced building operation contributes to resilience, loss prevention, and long-term asset value. Capstone Project Tasks and Deliverables: The capstone team will design and test a proof-of-concept "smart commissioning app" (e.g., a Python-based toolbox) that connects building-performance data with indicators of operational and insurance risk. The team will work with real commissioning data, potentially in collaboration with Penn State's Office of Physical Plant (OPP), to apply and test their methods on one or two campus buildings managed by OPP. Students will develop simple diagnostic algorithms, visualize fault-detection results, and calculate how these improvements translate into quantifiable risk or cost reductions. The resulting tool will serve as an initial prototype showing how smart commissioning can directly support risk-informed decision-making, demonstrate measurable return on investment, and lay the groundwork for future entrepreneurial or research extensions. Project Mentors: The mentors of this project are Dr. Nan Zhu, Associate Professor of Risk Management in the Department of Risk Management at Smeal College of Business, and Dr. Jin Wen, Professor and Department Head of the Department of Architectural Engineering at the College of Engineering. Team Composition: In addition to the primary Learning Factory Capstone team, this project will be supported by students from the Departments of Architectural Engineering and Risk Management. Together, all students will form a "virtual team" that collaborates across disciplines to address the technical, analytical, and financial dimensions of the project. This structure will give the capstone team access to broader expertise in building systems, data analytics, and risk modeling, while providing all participating students with an opportunity to contribute meaningfully to a complex, real-world engineering problem. This project is sponsored by the Penn State Cocoziello Institute of Real Estate Innovation.
