Machine learning based control systems for heating ventilation and cooling systems

A method by at least one processor, the method comprising:

• receiving flow data characterizing flow rates of a first fluid over a time range;
• receiving humidity data characterizing humidity levels over the time range;
• receiving temperature data characterizing temperatures over the time range;
• generating a training set of features based on the flow data, the humidity data, and the temperature data;
• training a machine learning process based on the training set of features;
• and
• storing machine learning model data characterizing the trained machine learning process in a data repository.

A system comprising:

• a conditioner sub-system configured to condition a stream of air based on a conditioning fluid;
• a regenerator sub-system configured to remove heat from the conditioning fluid;
• a controller configured to adjust at least one control setting of one or more of the conditioner sub-system and the regenerator sub-system;
• and
• a computing device communicatively coupled to the controller, the computing device configured to:
• receive, from at least one sensor, sensor data characterizing at least one of:
• a temperature, a humidity level, and a flow rate;
• generate an inference set of features based on the sensor data;
• apply a trained machine learning process to the inference set of features to generate output data characterizing a predicted operational value of one or more of the conditioner sub-system and the regenerator sub-system during a future temporal interval;
• and
• based on the output data, transmit a signal to the controller to adjust the at least one control setting.