System and method for data-driven control of an air-conditioning system

A method for controlling an operation of an air-conditioning system including a heat exchanger, comprising:

• executing a neural network trained to simulate an operation of the heat exchanger for a test control input, to produce an output of the simulation based on historical data defining a state of the heat exchanger, the historical data includes a sequence of historical control inputs provided to the heat exchanger and a sequence of historical outputs of the operation of the heat exchanger corresponding to the sequence of historical control inputs, wherein the neural network includes:
• a first arm configured to:
• process the test control input appended to the sequence of historical control inputs with a first combination of convolutional and recurrent networks trained to extract control features indicative of variation of the test control input from the historical control inputs;
• a second arm configured to:
• process the sequence of historical control inputs paired with the sequence of historical outputs with a second combination of convolutional and recurrent networks trained to extract output features indicative of dynamical coupling between inputs and corresponding output of the operation of the heat exchanger;
• and
• a third arm configured to process the control features and the output features to predict a test output of the operation of the heat exchanger corresponding to the test control input, wherein the third arm is a fully connected deep neural network accepting a tensor including the extracted control features and the extracted output features;
• determining a control command to the air-conditioning system based on the predicted test output of the simulation of the operation of the heat exchanger for the test control input;
• and
• transmitting the determined control command to an actuator of the air-conditioning system.

A non-transitory computer-readable medium having stored thereon computer-executable instructions, which when executed by a computer, cause the computer to execute operations, the operations comprising:

• executing a neural network trained to simulate an operation of the heat exchanger for a test control input, to produce an output of the simulation based on historical data defining a state of the heat exchanger, the historical data includes a sequence of historical control inputs provided to the heat exchanger and a sequence of historical outputs of the operation of the heat exchanger corresponding to the sequence of historical control inputs, wherein the neural network includes:
• a first arm configured to:
• process the test control input appended to the sequence of historical control inputs with a first combination of convolutional and recurrent networks trained to extract control features indicative of variation of the test control input from the historical control inputs;
• a second arm configured to:
• process the sequence of historical control inputs paired with the sequence of historical outputs with a second combination of convolutional and recurrent networks trained to extract output features indicative of dynamical coupling between inputs and corresponding output of the operation of the heat exchanger;
• and
• a third arm configured to process the control features and the output features to predict a test output of the operation of the heat exchanger corresponding to the test control input, wherein the third arm is a fully connected deep neural network accepting a tensor including the extracted control features and the extracted output features;
• determining a control command to the air-conditioning system based on the predicted test output of the simulation of the operation of the heat exchanger for the test control input;
• and
• transmitting the determined control command to an actuator of the air-conditioning system.