System and method for optimizing hvac systems in large institutions using wireless presence data, scheduling data, and predictive analytics

Abstract

Systems and methods for optimizing HVAC systems in large institutions are provided. Various embodiments of the technology provide systems and methods for determining and predicting building occupancy more accurately and in real-time to enhance energy usage and occupant comfort. Embodiments include a system and method that uses existing Wi-Fi infrastructure to collect wireless presence data, processes this data in a cloud-based environment, and employs machine learning algorithms to forecast occupancy. The system integrates scheduling data and can also incorporate CO2 sensor data to refine these predictions and dynamically adjust HVAC set points via the BACnet protocol.

What is claimed IS: 
     
         1 . A method for generating environmental control input data for a building management system (BMS) in a building, the method comprising:
 receiving, by a computing system, wireless local area networking (WLAN) data from a plurality of wireless access points (APs) within a building, the WLAN data associated with a plurality of user devices present in different zones of the building;   processing, by the computing system, the WLAN data to determine a real-time occupancy count for each of the different zones within the building;   forecasting, by the computing system using machine learning algorithms, a future occupancy count for each of the different zones for a future time period, wherein the forecasting is based on historical WLAN data, historical utilization data, and scheduled event data associated with each zone; and   generating, by the computing system, environmental control set point data for the BMS based on the real-time occupancy count and the forecasted future occupancy count for each of the different zones.   
     
     
         2 . The method of  claim 1 , further comprising incorporating, by the computing system, environmental sensor data comprising carbon dioxide (CO 2 ) levels into the generation of the environmental control set point data. 
     
     
         3 . The method of  claim 1 , further comprising determining, by the computing system, an adjusted occupancy level for at least one zone by applying a buffer to at least one of the real-time occupancy count or the forecasted future occupancy count for the at least one zone, wherein the environmental control set point data is further based on the adjusted occupancy level. 
     
     
         4 . The method of  claim 1 , further comprising identifying and predicting, by the computing system, recurring occupancy patterns for unscheduled spaces within the different zones based on historical WLAN data, wherein the environmental control set point data is further based on the predicted recurring occupancy patterns for unscheduled spaces. 
     
     
         5 . The method of  claim 1 , further comprising generating, by the computing system, input data for optimizing a geothermal loop system, the input data based on the forecasted future occupancy count. 
     
     
         6 . The method of  claim 1 , wherein processing the WLAN data further comprises employing, by the computing system, a device ranking algorithm to identify a primary device for each unique user based on criteria including connection time, number of wireless access points connected, and device type. 
     
     
         7 . The method of  claim 1 , wherein processing the WLAN data and forecasting the future occupancy count further comprises utilizing, by the computing system, stored detailed zone information comprising at least one of maximum capacity, building name, floor level, usage types, square footage, or hours of operation for each of the different zones. 
     
     
         8 . The method of  claim 1 , further comprising calculating and storing, by the computing system, a utilization rate for each of the different zones, the utilization rate based on the real-time occupancy count over time and designated hours of operation for each zone. 
     
     
         9 . A system for generating environmental control input data for a building management system (BMS) in a building, the system comprising:
 a processor; and   a non-transitory computer readable medium storing instructions translatable by the processor, the instructions when translated by the processor perform:
 receiving, by a computing system, wireless local area networking (WLAN) data from a plurality of wireless access points (APs) within a building, the WLAN data associated with a plurality of user devices present in different zones of the building; 
 processing, by the computing system, the WLAN data to determine a real-time occupancy count for each of the different zones within the building; 
 forecasting, by the computing system using machine learning algorithms, a future occupancy count for each of the different zones for a future time period, wherein the forecasting is based on historical WLAN data, historical utilization data, and scheduled event data associated with each zone; and 
 generating, by the computing system, environmental control set point data for the BMS based on the real-time occupancy count and the forecasted future occupancy count for each of the different zones. 
   
     
     
         10 . The system of  claim 9 , further comprising incorporating, by the computing system, environmental sensor data comprising carbon dioxide (CO2) levels into the generation of the environmental control set point data. 
     
     
         11 . The system of  claim 9 , further comprising determining, by the computing system, an adjusted occupancy level for at least one zone by applying a buffer to at least one of the real-time occupancy count or the forecasted future occupancy count for the at least one zone, wherein the environmental control set point data is further based on the adjusted occupancy level. 
     
     
         12 . The system of  claim 9 , further comprising identifying and predicting, by the computing system, recurring occupancy patterns for unscheduled spaces within the different zones based on historical WLAN data, wherein the environmental control set point data is further based on the predicted recurring occupancy patterns for unscheduled spaces. 
     
     
         13 . The system of  claim 9 , further comprising generating, by the computing system, input data for optimizing a geothermal loop system, the input data based on the forecasted future occupancy count. 
     
     
         14 . The system of  claim 9 , wherein processing the WLAN data further comprises employing, by the computing system, a device ranking algorithm to identify a primary device for each unique user based on criteria including connection time, number of wireless access points connected, and device type. 
     
     
         15 . The system of  claim 9 , wherein processing the WLAN data and forecasting the future occupancy count further comprises utilizing, by the computing system, stored detailed zone information comprising at least one of maximum capacity, building name, floor level, usage types, square footage, or hours of operation for each of the different zones. 
     
     
         16 . The system of  claim 9 , further comprising calculating and storing, by the computing system, a utilization rate for each of the different zones, the utilization rate based on the real-time occupancy count over time and designated hours of operation for each zone. 
     
     
         17 . A computer program product comprising a non-transitory computer readable medium storing instructions translatable by a processor, the instructions when translated by the processor perform, in an enterprise computing network environment:
 receiving, by a computing system, wireless local area networking (WLAN) data from a plurality of wireless access points (APs) within a building, the WLAN data associated with a plurality of user devices present in different zones of the building;   processing, by the computing system, the WLAN data to determine a real-time occupancy count for each of the different zones within the building;   forecasting, by the computing system using machine learning algorithms, a future occupancy count for each of the different zones for a future time period, wherein the forecasting is based on historical WLAN data, historical utilization data, and scheduled event data associated with each zone; and   generating, by the computing system, environmental control set point data for the BMS based on the real-time occupancy count and the forecasted future occupancy count for each of the different zones.   
     
     
         18 . The computer program product of  claim 17 , further comprising incorporating, by the computing system, environmental sensor data comprising carbon dioxide (CO2) levels into the generation of the environmental control set point data. 
     
     
         19 . The computer program product of  claim 17 , further comprising determining, by the computing system, an adjusted occupancy level for at least one zone by applying a buffer to at least one of the real-time occupancy count or the forecasted future occupancy count for the at least one zone, wherein the environmental control set point data is further based on the adjusted occupancy level. 
     
     
         20 . The computer program product of  claim 17 , further comprising identifying and predicting, by the computing system, recurring occupancy patterns for unscheduled spaces within the different zones based on historical WLAN data, wherein the environmental control set point data is further based on the predicted recurring occupancy patterns for unscheduled spaces.