| CPC F24F 11/89 (2018.01) [F24F 11/63 (2018.01); F24F 2110/10 (2018.01); F24F 2110/50 (2018.01); F24F 2120/10 (2018.01)] | 13 Claims |
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1. A device for controlling heating, ventilation, and air conditioning (HVAC) systems, comprising:
at least one thermal sensing camera configured to capture a plurality of images of a space to determine a temperature of the space;
a control system in communication with the at least one thermal sensing camera, including:
a computer in communication with the at least one thermal sensing camera, wherein the computer includes at least one processor and at least one memory in communication with the at least one processor;
a controller in communication with the computer, wherein the at least one memory stores computer instructions configured to instruct the processor to communicate with the controller; and
a machine learning model in communication with at least one of the computer or the at least one thermal sensing camera,
wherein the machine learning model includes an artificial neural network configured to analyze the images of the plurality of images captured by the at least one thermal sensing camera, wherein the machine learning model includes a convolutional neural network (CNN) in communication with the artificial neural network, wherein the CNN is configured to parse the images of the plurality of images to determine the temperature in the space,
wherein the computer employs the artificial neural network of the machine learning model to determine an amount of thermogenesis for a person or people occupying the space and create a three dimensional voxel model of the space annotated with the location of the person or people occupying the space and the amount of thermogenesis for the person or people occupying the space,
wherein the machine learning model is trained to determine the amount of heating or cooling output needed to maintain a predetermined temperature in the space by:
training the machine learning model on a first data set to determine the amount of heating or cooling output needed to maintain a predetermined temperature in the space based on the amount of thermogenesis for the person or people occupying the space; and
iteratively training the machine learning model on at least a second data set and a third data set to determine the amount of heating or cooling output needed to maintain the predetermined temperature in the space based on the amount of thermogenesis for the person or people occupying the space, wherein iteratively training the machine learning model on at least the second data set and the third data set increases predictive accuracy of the machine learning model with respect to training the machine learning model on the first data set,
wherein the amount of heating or cooling output needed to maintain the predetermined temperature in the space is determined by employing the iteratively trained machine learning model,
wherein the iteratively trained machine learning model is configured to determine a number of people occupying the space,
wherein the controller is configured to communicate with a heating, ventilation, and air conditioning (HVAC) system, wherein the HVAC system is configured to control the temperature in the space by heating or cooling the space, wherein the controller is configured to transmit the determined amount of heating or cooling output needed to maintain the predetermined temperature in the space to the HVAC system to maintain the predetermined temperature in the space.
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