Systems and methods for managing energy and air quality
A management system makes decisions using local and national outdoor air quality data, public health data, and building and occupant information. The system addresses the balance between healthy air and energy efficiency.
1. A method to manage air quality in a space, comprising:
a processor;
sensors to collect air flow data, energy consumption data, and air quality data in the outdoor air dampers to control an outdoor air exchange rate;
a fan to control an air changes per hour (ACH);
an air damper to control an outdoor air fraction (OAF); and
code executed by the processor to:
receive air quality data from related building zones;
receive at one or more predetermined intervals at least one of government environmental and health data;
calculate an indoor contamination level and an outdoor contamination level by determining the number of particulate matters at or below 2.5 microns, fan speed data, damper position data, the air quality data from the related building zones, the government data, the air flow data, the energy consumption data, and the air quality data; and
when indoor contamination level is at or above 15 micrograms, mitigate health risk of an occupant of the space by:
when outdoor contamination level is at or below a low threshold of 15 micrograms, increase the OAF with the air damper;
when outdoor contamination level is above the low threshold and below a moderate threshold of 40 micrograms, decrease OAF with the air damper and linearly increase the ACH with the fan;
when indoor contamination level exceeds the outdoor contamination level, increase the ACH with the fan and OAF with the air damper; and
when the indoor contamination and the outdoor contamination are above a high threshold, increase the ACH with the fan and linearly decrease the OAF with the air damper.
2. The system of claim 1 , wherein the space comprises a commercial building, a floor of the building, a house, or a room, further comprising using future third-party forecasted air quality conditions to predictively control air quality.
3. The system of claim 1 , comprising code for quantifying relationships between exposure to indoor contaminants and the health of the building occupants.
4. The system of claim 1 , comprising code for generating indoor air contaminant risk-mitigation control strategies based on data from a community in geometric proximity or a neighborhood.
5. The system of claim 1 , comprising code for modeling airflow patterns for a ventilation system to determine placement of air quality sensors at predetermined air locations.
6. The system of claim 1 , comprising code for assessing energy consumption.
7. The system of claim 1 , comprising code for maintaining a predetermined pressure in each space.
8. The system of claim 1 , comprising code for adjusting (OAF) or (ACH) based on Outdoor and Indoor air contaminants informed by an acceptable risk threshold.
9. The system of claim 1 , comprising code for adjusting flow and ACH based on occupants and public health infection risks.
10. The system of claim 1 , comprising code for determining a risk of viral transmission through aerosols using zone or HVAC data and public positivity rates and a risk profile.
11. The system of claim 1 , comprising code for controlling the air management system to isolate and evacuate indoor air contaminants based on indoor air quality sensors.
12. The system of claim 1 , comprising code for monitoring zone health, initiating local control commands and sending notifications via email.
13. The system of claim 1 , comprising code for adjusting air flow in response to a COVID event or community health event.
14. The system of claim 1 , comprising code for applying machine learning to control the air management system based on updated sensor, environmental and public health data.
15. A system to manage air quality in a space, comprising:
one or more sensors positioned in the space;
an air management system;
a processor coupled to the one or more sensors and air management system with code for:
receive air quality data from related building zones;
receive at one or more predetermined intervals at least one of government environmental and health data;
calculate an indoor contamination level and an outdoor contamination level by determining the number of particulate matters at or below 2.5 microns, fan speed data, damper position data, the air quality data from the related building zones, the government data, the air flow data, the energy consumption data, and the air quality data; and
when indoor contamination level is at or above 15 micrograms, mitigate health risk of an occupant of the space by:
when outdoor contamination level is at or below a low threshold of 15 micrograms, increase the OAF with the air damper;
when outdoor contamination level is above the low threshold and below a moderate threshold of 40 micrograms, decrease OAF with the air damper and linearly increase the ACH with the fan;
when indoor contamination level exceeds the outdoor contamination level, increase the ACH with the fan and OAF with the air damper; and
when the indoor contamination and the outdoor contamination are above a high threshold, increase the ACH with the fan and linearly decrease the OAF with the air damper.
16. The system of claim 15 , wherein the space comprises a commercial building, a floor of the building, a house, or a room.
17. The system of claim 15 , comprising code for quantifying relationships between exposure to indoor contaminants and the health of the building occupants.
18. The system of claim 15 , comprising code for generating indoor air contaminant risk-mitigation control strategies for building HVAC equipment.
19. The system of claim 15 , comprising neural network code for learning about air quality.
20. The system of claim 19 , comprising a server with code for processing energy consumption or efficiency data, wherein the neural network receives airflow data from sensors in the space, and optimizes fan speed for air quality when the air quality is below a threshold, and otherwise manages the fan for energy efficiency.