Cooling unit energy optimization via smart supply air temperature setpoint control
The present disclosure relates to a system for controlling a supply air temperature adjustment for a cooling unit to optimize operation of the cooling unit with respect to at least one of room air temperature and humidity requirements. The system uses a controller for implementing: a machine learning module configured to select which portion or portions of acquired data pertaining to operation of the cooling unit will be utilized; and a neural network model which uses information 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, and wherein the neural network model uses information supplied by the machine learning module for generating an output. The controller also implements 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 a supply air temperature set point for use by the cooling unit which optimizes an operating parameter of the cooling unit.
1. A system for controlling a supply air temperature adjustment for a cooling unit to optimize operation of the cooling unit with respect to at least one of room air temperature and humidity requirements, the system comprising:
a controller for implementing:
a machine learning module configured to select which portion or portions of acquired data pertaining to operation of the cooling unit will be utilized;
a neural network model which uses information 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 information supplied by the machine learning module for generating an output; 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 an objective function, to produce a supply air temperature set point for use by the cooling unit which optimizes an operating parameter of the cooling unit.
2. The system of claim 1 , wherein the neural network model comprises a Unit Power neural network module representing cooling unit power consumption, and wherein the Unit Power neural network module receives inputs from at least one other neural network model included within the system.
3. The system of claim 2 , wherein the neural network model further comprises 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 to the Unit Power neural network module.
4. The system of claim 1 , wherein the neural network model further comprises a supply air temperature (SAT) neural network model for representing a temperature of air being generated and output by the cooling unit.
5. The system of claim 1 , wherein the neural network model further comprises a fan percentage neural network model which represents a percentage of maximum fan speed that a fan of the cooling unit is running at.
6. The system of claim 1 , wherein the neural network model comprises a cooling capacity (CC) neural network model for representing an overall cooling capacity, in percentage units, of the cooling unit.
7. The system of claim 1 , wherein the optimization module receives data from at least one data source and uses the data together with the output from the neural network model when implementing the global optimization routine.
8. 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 configured to implement:
a machine learning module configured to select which portion or portions of acquired data pertaining to operation of the cooling unit are utilized;
a neural network model which uses information 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 information supplied by the machine learning module for generating an output;
the neural network model having a Unit Power neural network module which receives inputs from at least one 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 to the unit power neural network module; or
a return air temperature (RAT) neural network model for representing a temperature of air being returned to the cooling unit; or
a supply air temperature (SAT) neural network model for representing a temperature of air being generated and output by the cooling unit; 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 an objective function, to produce a supply air temperature set point for use by the cooling unit which optimizes an operating parameter of the cooling unit.
9. The system of claim 8 , wherein the neural network model further comprises a fan percentage neural network model which represents a percentage of maximum fan speed that a fan of the cooling unit is running at.
10. The system of claim 8 , wherein the neural network model further comprises a cooling capacity (CC) neural network model for representing an overall cooling capacity, in percentage units, of the cooling unit.
11. The system of claim 8 , wherein the optimization module receives data from at least one data source and uses the data together with the output from the neural network model when implementing the global optimization routine.
12. A system for controlling a supply air temperature adjustment for a data center cooling unit to optimize operation of the cooling unit with respect to room air temperature and humidity requirements, the system comprising:
a controller configured to implement:
a machine learning module configured to select which portion or portions of acquired data pertaining to operation of the cooling unit are utilized;
a neural network model which uses information 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 including:
a Unit Power neural network model representing cooling unit power consumption;
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 to the unit power neural network model;
a return air temperature (RAT) neural network model for representing a temperature of air being returned to a given one of the cooling units;
a supply air temperature (SAT) neural network model for representing a temperature of air being generated and output by the cooling unit;
a fan percentage neural network model which represents a percentage of maximum fan speed that a fan of the cooling unit is running at;
a cooling capacity (CC) neural network model for representing an overall cooling capacity, in percentage units, of the cooling unit;
the Unit Power neural network model using information supplied by all of the RET, RAT, SAT, fan percentage and CC neural network models in providing an output; 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 an objective function, and which produces a supply air temperature set point for use by the cooling unit which optimizes an operating parameter of the cooling unit.
13. The system of claim 12 , wherein the optimization module receives data from at least one data source and uses the data together with the output from the neural network model when implementing the global optimization routine.