A system for controlling a supply air temperature adjustment for a cooling unit to optimize operation of the cooling unit with respect to room air temperature and humidity requirements, the system comprising: a controller for implementing: a machine learning module configured to select a portion of data acquired by the controller pertaining to operation of the cooling unit; a neural network model which uses the data supplied by the machine learning module and learns an operational behavior of the cooling unit, and wherein the machine learning module performs supervised learning and regression for the neural network model; the neural network model using the data supplied by the machine learning module for generating an output; the neural network model having a unit power neural network model which receives inputs from other neural network models including: a remote air temperature, RET, neural network model representing a rack inlet temperature of the cooling unit, the RET neural network model providing an output representing the rack inlet temperature as the data to the unit power neural network model; and a return air temperature, RAT neural network model for representing a temperature of air being returned to a given one of the cooling units, wherein the temperature of the air being returned to the cooling unit represents the data; and a supply air temperature, SAT, neural network model for representing a temperature of air being generated and output by the cooling unit, wherein the output representing the temperature of air being generated and output by the cooling unit represents the data; and an optimization module which receives the output from the neural network model and which implements a global optimization routine, using unit power consumption of the cooling unit as the objective function, to produce an optimized supply air temperature set point for use by the cooling unit. a machine learning module configured to select a portion of data acquired by the controller pertaining to operation of the cooling unit; a neural network model which uses the data supplied by the machine learning module and learns an operational behavior of the cooling unit, and wherein the machine learning module performs supervised learning and regression for the neural network model; the neural network model using the data supplied by the machine learning module for generating an output; the neural network model having a unit power neural network model which receives inputs from other neural network models including: a remote air temperature, RET, neural network model representing a rack inlet temperature of the cooling unit, the RET neural network model providing an output representing the rack inlet temperature as the data to the unit power neural network model; and a return air temperature, RAT neural network model for representing a temperature of air being returned to a given one of the cooling units, wherein the temperature of the air being returned to the cooling unit represents the data; and a supply air temperature, SAT, neural network model for representing a temperature of air being generated and output by the cooling unit, wherein the output representing the temperature of air being generated and output by the cooling unit represents the data; and a remote air temperature, RET, neural network model representing a rack inlet temperature of the cooling unit, the RET neural network model providing an output representing the rack inlet temperature as the data to the unit power neural network model; and a return air temperature, RAT neural network model for representing a temperature of air being returned to a given one of the cooling units, wherein the temperature of the air being returned to the cooling unit represents the data; and a supply air temperature, SAT, neural network model for representing a temperature of air being generated and output by the cooling unit, wherein the output representing the temperature of air being generated and output by the cooling unit represents the data; and an optimization module which receives the output from the neural network model and which implements a global optimization routine, using unit power consumption of the cooling unit as the objective function, to produce an optimized supply air temperature set point for use by the cooling unit.