1 Toward Making Ventilation Decisions Based on Expected Outcomes: A Flexible Multi-Criteria Framework
2 Measured Space-Conditioning Energy and Indoor RH in a Mechanically-Ventilated Lab Home with Fixed and Variable-Capacity Cooling Systems Located in a Hot and Humid Climate
3 Modeling Monetization of Collateral IAQ Improvements from UVGI for Coil Cleaning
4 Data Driven Persistent Monitoring of Indoor Air Systems
Persistent monitoring of Indoor Air Quality (IAQ) within and around buildings and structures is critical to reduce risk of indoor health concerns. Specifically, IAQ issues in large integrated buildings may stem from inadequate ventilation and/or faults in the complex HVAC systems that together with control and communication systems can be considered as complex Cyber Physical Systems (CPSs). We propose a data-driven framework for monitoring distributed complex CPSs that reliably captures cyber and physical sub-system behaviors as well as their interaction characteristics. Using such learning methods, we aim to identify the anomalies and faults at an early stage such that necessary mitigation measures can be pursued in time. A fault in the HVAC system may be due to both physical and cyber anomalies affecting the operational goals of the building system. The proposed technique involves modeling of cyber and physical entities using probabilistic graphical models that capture individual characteristics of the sub-system and causal dependencies among different sub-systems. The proposed model can be trained using nominal historical data and then can be used to monitor the HVAC system and IAQ during regular operation. Our method is validated with a case study on an integrated “zero energy” house built for the 2009 Solar Decathlon that has been used both as an experimental test bed and office for more than 3 years.