System for controlling heating ventilation and air conditioning (HVAC) systems

1 . A system for controlling heating, ventilation, and air conditioning (HVAC) systems, comprising:

at least one camera configured to capture a plurality of images of a space to analyze the thermodynamic characteristics of the space;

a control system in communication with the at least one camera, including:

a computer in communication with the at least one camera, wherein the computer includes at least one processor and at least one memory in communication with the at least one processor; and

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 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 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 analyze the thermodynamic characteristics of 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,

wherein the machine learning model is trained to determine the amount 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 at least one of the first data set, the second data set, or the third data set includes a three dimensional room model annotated with detected thermal generating items, locations of the thermal generating items, and heat emissions levels of the thermal generating items,

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; and

a heating, ventilation, and air conditioning (HVAC) system in communication with the controller, 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.

2 . The system of claim 1 , further including a temperature sensor in communication with the computer, wherein the temperature sensor is configured to directly measure the current temperature in the space.

3 . The system of claim 2 , wherein the temperature sensor is a digital temperature sensor, an analog temperature sensor, a thermocouple, a resistance temperature detector, a USB temperature sensor, a Wi-Fi® temperature sensor, or a Bluetooth® temperature sensor.

4 . The system of claim 1 , wherein the iteratively trained machine learning model is configured to:

detect an object or objects occupying the space;

determine an amount of heat released by the object or objects occupying the space;

determine an amount of heat absorbed by the object or objects occupying the space;

determine a future temperature in the space relative to a current temperature based on the amount of heat released by the object or objects occupying the space and the amount of heat absorbed by the object or objects occupying the space; and

determine the amount of heating or cooling output needed to maintain the predetermined temperature or the adjusted predetermined temperature in the space based on the amount of heat released by the object or objects occupying the space and the amount of heat absorbed by the object or objects occupying the space.

5 . The system of claim 1 , wherein the at least one camera includes a camera configured to capture video images, and wherein the images of the plurality of images are part of a video image.

6 . The system of claim 1 , wherein the at least one camera includes at least one of a thermal imaging camera, an infrared camera, a thermographic camera, a laser thermometer camera, a radiometric camera, or a thermal sensor camera.

7 . The system of claim 1 , further including a wireless transmitter configured to connect the controller with the HVAC system.

8 . The system of claim 1 , wherein the controller is configured to communicate with the HVAC system by a Wi-Fi®, Bluetooth® or cellular network connection.

9 . The system of claim 1 , wherein the HVAC system includes a wireless transmitter configured to communicate with the controller.

10 . The system of claim 1 , further including a wireless transmitter configured to communicate with a cloud-based server.

11 . The system of claim 10 , wherein the wireless transmitter is configured to communicate with the cloud-based server through an internet or cellular network connection.

12 . The system of claim 1 , wherein the number of people occupying the space is determined by the iteratively trained machine learning model by analyzing the images of the plurality of images captured by the at least one camera.

13 . The system of claim 12 , wherein the iteratively trained machine learning model is further configured to perform a 3-dimensional volumetric thermal analysis of the space by employing the images of the plurality of images captured by the at least one camera to determine the amount of thermogenesis for the person or people occupying the space.