Computing device and method for inferring an airflow of a vav appliance operating in an area of a building

Abstract

A method and computing device for inferring an airflow of a controlled appliance operating in an area of a building. The computing device stores a predictive model. The computing device determines a measured airflow of the controlled appliance and a plurality of consecutive temperature measurements in the area. The computing device executes a neural network inference engine using the predictive model for inferring an inferred airflow based on inputs. The inputs comprise the measured airflow and the plurality of consecutive temperature measurements. The inputs may further include at least one of a plurality of consecutive humidity level measurements in the area and a plurality of consecutive carbon dioxide (CO2) level measurements in the area. For instance, the controlled appliance is a Variable Air Volume (VAV) appliance and a K factor of the VAV appliance is calculated based on the inferred airflow.

1 . A method for calculating a new K factor K 1  of a Variable Air Volume (VAV) appliance, comprising:
 determining, with a computing device, a measured airflow Q m  of the VAV appliance;   determining, with the computing device, a plurality of consecutive temperature measurements in an area of a building;   executing, with the computing device, a neural network inference engine using a predictive model for inferring an inferred airflow Q i  based on inputs, the inputs comprising the measured airflow Q m  and the plurality of consecutive temperature measurements;   calculating, with the computing device, K 1  using the equation K 1 =K 0 *(Q m /Q i ) 2 , wherein K 0  is a K factor currently used by the VAV appliance;   configuring the VAV appliance to use at least K 1  to auto-regulate at least one operation of the VAV appliance.   
     
     
         2 . (canceled) 
     
     
         3 . (canceled) 
     
     
         4 . (canceled) 
     
     
         5 . The method of  claim 1 , wherein determining with the computing device the measured airflow Q m  of the VAV appliance consists in receiving the measured airflow from the VAV appliance. 
     
     
         6 . The method of  claim 1 , wherein the inputs further comprise at least one of the following: a plurality of consecutive humidity level measurements in the area determined by the computing device, a plurality of consecutive carbon dioxide (CO2) level measurements in the area determined by the computing device, and a combination thereof. 
     
     
         7 . The method of  claim 1 , wherein the predictive model comprises weights used by the neural network inference engine for inferring the inferred airflow Q i  based on the inputs. 
     
     
         8 . A non-transitory computer program readable medium comprising computer readable instructions when executed by a processor of a computing device cause the computing device to:
 determine a measured airflow Q m  of a Variable Air Volume (VAV) appliance;   determine a plurality of consecutive temperature measurements in an area of a building;   execute a neural network inference engine using a predictive model for inferring an inferred airflow Q i  based on inputs, the inputs comprising the measured airflow Q m  and the plurality of consecutive temperature measurements;   calculate a new K factor K 1  of the VAV appliance using the equation K 1 =K 0  (Q m /Q i ) 2  wherein K 0  is a K factor currently used by the VAV appliance; and   transmit a configuration command comprising K 1  to the VAV appliance, wherein the configuration command is configured to cause the VAV appliance to use at least K 1  to auto-regulate at least one operation of the VAV appliance for automatically reconfiguring the VAV appliance with the new K factor K 1 , K 1  being used for auto-regulating operations of the VAV appliance.   
     
     
         9 . (canceled) 
     
     
         10 . (canceled) 
     
     
         11 . (canceled) 
     
     
         12 . The computer program of  claim 8 , wherein the inputs further comprise at least one of the following: a plurality of consecutive humidity level measurements in the area determined by the computing device, a plurality of consecutive carbon dioxide (CO2) level measurements in the area determined by the computing device, and a combination thereof. 
     
     
         13 . The computer program of  claim 8 , wherein the predictive model comprises weights used by the neural network inference engine for inferring the inferred airflow Q i  based on the inputs. 
     
     
         14 . A computing device for inferring an airflow Q i  of a Variable Air Volume (VAV) appliance operating in an area of a building, the computing device comprising:
 memory for storing a predictive model; and   a processing unit for:
 determining a measured airflow Q m  of the VAV appliance; 
 determining a plurality of consecutive temperature measurements in the area; 
 executing a neural network inference engine using the predictive model for inferring an inferred airflow Q i  based on inputs, the inputs comprising the measured airflow and the plurality of consecutive temperature measurements 
 calculate a new K factor K 1  of the VAV appliance using the equation K 1 =K 0  (Q m /Q i ) 2  wherein K 0  is a K factor currently used by the VAV appliance; and 
 transmit a configuration command comprising K 1  to the VAV appliance, wherein the configuration command is configured to cause the VAV appliance to use at least K 1  to auto-regulate at least one operation of the VAV appliance for automatically reconfiguring the VAV appliance with the new K factor K 1 , K 1  being used for auto-regulating operations of the VAV appliance. 
   
     
     
         15 . (canceled) 
     
     
         16 . (canceled) 
     
     
         17 . (canceled) 
     
     
         18 . The computing device of  claim 14 , wherein determining by the processing unit a measured airflow Q m  of the VAV appliance consists in receiving the measured airflow from the VAV appliance via a communication interface of the computing device. 
     
     
         19 . The computing device of  claim 14 , wherein the inputs further comprise at least one of the following: a plurality of consecutive humidity level measurements in the area, a plurality of consecutive carbon dioxide (CO2) level measurements in the area, and a combination thereof. 
     
     
         20 . The computing device of  claim 14 , wherein the predictive model comprises weights used by the neural network inference engine for inferring the inferred airflow Q i  based on the inputs.