Machine learning-based air handler and control method thereof

A method of controlling a machine learning-based air handler, the method comprising:

• collecting first data on an air quality of air flowing into the air handler;
• controlling a purification part comprising at least one charging part having at least one tube comprising an anode and a cathode and a dust collecting part that collects ionized substances with a positive charge and a negative charge based on the first data;
• collecting second data on an air quality of air passing through the purification part;
• and
• controlling the purification part according to a machine-learning model generated based on a setting value related to a control of the purification part, the first data, the second data, and an input value including a number of at least one tube, a location of the at least one tube, and a size of the dust collecting part,
• wherein the setting value related to the control of the purification part comprises at least one of a voltage value applied to the anode and a voltage value applied to the cathode, and
• wherein the at least one charging part ionizes substance by emitting electromagnetic wave having a wavelength in a range between 0.1 nanometers and 100 nanometers using the cathode composed of carbon nanotube.