Hvac efficiency boosting fan system, apparatus and method

Abstract

An HVAC efficiency boosting fan system, device and methods are provided. A booster fan unit is installed in an air duct adjacent to a register. The booster fan unit includes a blower for moving air, a thermistor for measuring air temperature in the duct, an accelerometer for detecting vibrations caused by HVAC operation and a microphone for detecting noise by persons in proximity to the booster fan unit. The booster fan unit is configured to implement trained machine learning to predict an operational state of the HVAC unit based on inputs received from the thermistor, and accelerometer and adjust operation of the blower based on the predicted operational state and measured air temperature in the duct. Also provided is a method for estimating an ambient room temperature based on a measured air temperature in an air duct.

1 . A booster fan unit for installation in an air duct adjacent a register, comprising:
 a) a housing having at least one opening extending through the housing, the housing positionable in the air duct with the opening in alignment with a flow path of the air duct;   b) a blower positioned in the opening for moving air through the opening along the flow path from a first side of the booster fan unit to a second side of the booster fan unit;   c) a temperature sensor in the housing to measure air temperature in the opening; and   d) a plurality of retaining arms attached to the housing and spaced apart from each other about the opening, each arm projecting from the housing generally parallel with the flow path to a terminal end projecting generally perpendicular to the flow path for positioning over a lip of the duct to retain the booster fan unit in the air duct adjacent the register.   
     
     
         2 . The booster fan unit of  claim 1 , wherein the blower is bidirectional. 
     
     
         3 . The booster fan unit of  claim 1 , wherein the blower is variable in speed. 
     
     
         4 . The booster fan unit of  claim 1 , wherein the terminal end is positionable between the register and a lip of the duct. 
     
     
         5 . The booster fan unit of  claim 1 , wherein each retaining arm is spring-biased to fictionally engage walls of the duct and bias the terminal ends into position over the lip of the duct. 
     
     
         6 . The booster fan unit of  claim 1 , wherein the terminal end of each retaining arm includes an aperture for receiving a fastener to securely attach the terminal end to at least one of the register and a surface adjacent the lip of the duct. 
     
     
         7 . The booster fan unit of  claim 1 , further comprising an accelerometer for detecting vibrations in the air duct caused by HVAC operation. 
     
     
         8 . The booster fan unit of  claim 1 , further comprising wireless network components for connecting the booster fan unit to a wireless network. 
     
     
         9 . The booster fan unit of  claim 1 , further comprising a processor configured to adjust operation of the blower based on inputs received from the temperature sensor. 
     
     
         10 . The booster fan unit of  claim 9 , further comprising memory coupled to the processor for storing processor-executable instructions including an artificial intelligence module, the artificial intelligence module comprising one or more machine learning algorithms trained to process inputs received from the temperature sensor to predict at least one of: an operational state of an HVAC unit and an ambient temperature in the room. 
     
     
         11 . A method for predicting an operational state of an HVAC unit, comprising:
 a) measuring temperature over time in an air duct to monitor a current temperature in the air duct relative to a prior temperature in the air duct, the prior temperature measured at a predetermined time interval prior to the current temperature;   b) based on (a):
 i) determining if the current temperature is greater than a sum of a heating tolerance parameter and the prior temperature, and the prior temperature is greater than a heating threshold, and if so, predicting that the HVAC unit is in a heating cycle; 
 ii) determining if the current temperature is greater than a sum of a cooling tolerance parameter and the prior temperature, and the prior temperature is less than a cooling threshold, and if so, predicting that the HVAC unit is in a cooling cycle; and 
   c) recording a predicted operational state of the HVAC unit based on (b).   
     
     
         12 . The method of  claim 11 , further comprising: determining a prediction history average, wherein a positive prediction history average establishes the HVAC unit is in the heating cycle and a negative prediction history average establishes the HVAC unit is in the cooling cycle. 
     
     
         13 . The method of  claim 12 , wherein the prediction history average is equal to a prediction history sum divided by a total number of predicted heating cycles and cooling cycles. 
     
     
         14 . The method of  claim 13 , wherein the total number of predicted heating cycles and cooling cycles includes predicted heating cycles and cooling cycles lasting at least five minutes in duration. 
     
     
         15 . A method for predicting ambient room temperature, comprising:
 a) measuring temperature over time in an air duct for a plurality of qualifying HVAC cycles;   b) calculating a first statistical temperature model for the plurality of qualifying HVAC cycles based on temperature measurements taken during a predetermined first time period following an end of each qualifying HVAC cycle;   c) calculating a second statistical temperature model for the plurality of qualifying HVAC cycles based on temperature measurements taken during a predetermined second time period following the first time period for each qualifying HVAC cycle; and   d) applying the first and the second statistical temperature models to a temperature measurement to estimate the ambient room temperature.   
     
     
         16 . The method of  claim 15 , wherein the temperature measurement in (d) is recorded at the end of a subsequent HVAC cycle following the plurality of qualifying HVAC cycles. 
     
     
         17 . The method of  claim 15 , wherein each qualifying HVAC cycle is preceded by a preceding HVAC cycle by at least ninety minutes and followed by a subsequent HVAC cycle at least thirty minutes later. 
     
     
         18 . The method of  claim 15 , wherein each of the first statistical temperature model and the second statistical temperature model comprises an averaged linear regression for the plurality of qualifying HVAC cycles. 
     
     
         19 . The method of  claim 15 , wherein the plurality of qualifying HVAC cycles comprises at least three qualifying HVAC cycles. 
     
     
         20 . The method of  claim 15 , wherein the first time period comprises a 30-minute time period and the second time period comprises a 60-minute time period.