Utilizing Live Sensor Data to Improve Indoor Air Quality via the Building Management System
- CEO
- Data Scientist
- Full-Stack Developer
Indoor air quality is crucial to maintaining a sustainable and resilient building. Controlling and ultimately forecasting indoor air quality requires cohesion among all building data. Mechanical systems should work on a unique sequence of operations to preserve human comfort levels and proper ventilation. ASHRAE’s indoor air quality guidance was created to slow the transmission of the COVID-19 virus via the HVAC system, but there is a need to define a healthy building in terms of proper air quality. We need to rely on real-time sensor arrays to augment our code minimum ventilation standards. The benchmark for success to reduce the transmission of the virus can be accomplished with IAQ sensors driving the HVAC controls for ventilation equipment. The Phase II proposal will build upon the Phase I data collected to analyze 1000 plus IAQ sensors. Phase I reviewed the technical detail of actual readings of PM2.5, CO2, temperature, TVOCs, and humidity to determine the optimal air quality for a teaching environment to minimize the transmission of the COVID-19 virus. It analyzed decay rates of the CO2 based on occupancy, air change rates, and ventilation rates in the classroom space. Phase I showed deficiencies in the speed of IAQ readings to BMS changes. A better ventilation management interface, such as the digital twin, could improve these deficiencies. Based on a year’s worth of data aggregation, further research opportunities presented themselves. TVOC and PM2.5 monitoring appeared unstable as readings regularly exceeded thresholds, or never exceeded thresholds respectively. Building operations, such as cleaning and mask-wearing, impacted readings. Furthermore, monthly reporting periods required subjective ratings to score the air quality of the school. This challenged the team to determine the optimal reporting period for a complete aggregation of the data to generate an accurate air quality score. These issues will need to be developed and implemented into Phase II. This extension of the proposal will start to analyze the energy consumption of building mechanical ventilation systems as well data presentation in the digital twin. This proof of concept will test ventilation effectiveness without requiring increased energy use. Integration of the digital twin viewer and database will be tied into the IoT sensors and building management systems. Energy meters will be mapped to assets. Live data will feed on energy meters and IoT sensors, and utility bills will be analyzed as ventilation increases. Current models project a higher energy usage and thereby carbon footprint increase. SYYCLOPS can build on the HVAC sequences of operations to reduce the energy usage of the HVAC systems via the digital twin interface.
- Research Organization:
- SYYCLOPS
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- SC0021829
- OSTI ID:
- 1959346
- Type / Phase:
- SBIR (Phase I)
- Report Number(s):
- DE-SC0021829
- Country of Publication:
- United States
- Language:
- English
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