A processing unit includes a plurality of subsystem control modules. Each subsystem control module includes a set of one or more inputs that receives a set of one or more external signals and a set of one or more monitored outputs from a hardware subsystem corresponding to the subsystem control module, and a set of configuration outputs for controlling one or more configuration settings of the hardware subsystem. The subsystem control module determines the one or more configuration settings based on the set of monitored outputs and on one or more targets derived from the set of external signals.

An industrial automation system is described including multiple devices, a master container node, a container node, and a control system. The devices may connect to an external application programming interface (API) of an external device (e.g., an electric power utility company), receive an API call from the external API indicative of a time window for monitoring electrical power consumption of the devices by the external API, and output one or more information technology (IT) commands in response to receiving the API call. The container node may be coupled to the master container node, wherein the container node may map the IT commands to operation technology (OT) commands. The control system may be coupled to the container node and the plurality of devices. The control system may implement the OT commands on the devices to perform one or more operations.

A method and apparatus for determining the suitability of a network configuration for a factory energy management system (FEMS) are provided. The method includes receiving an energy management level for obtaining the suitability of a network configuration for an FEMS, receiving second parameters according to the energy management level among first parameters set for the network configuration, receiving a communication characteristics matrix and a communication adaptability matrix for communication schemes usable for the network configuration, obtaining a communication characteristics metric matrix by normalizing the communication characteristics matrix, based on the second parameters and the communication characteristics matrix, obtaining a communication adaptability metric matrix by normalizing the communication adaptability matrix, based on the energy management level, the second parameters, and the communication adaptability matrix, and determining the suitability of the network configuration at the energy management level based on the communication characteristics metric matrix and the communication adaptability metric matrix.

A cell control apparatus is provided with a load calculation part which calculates an operational load including at least one of average power consumption, maximum power consumption, and a variable indicating a component lifetime of a manufacturing cell in a predetermined period. The cell control apparatus includes an operation adjustment part which performs first control for reducing at least one of a speed and an acceleration at which the manufacturing machine drives and second control for adjusting a ratio of time of successive operations in such a manner as to reduce the operational load in such an extent that the number of products manufactured in the predetermined period is not changed.

The present invention includes (1) inputting, into a model expression that defines relation between various operating values of a facility operating on a material to be rolled and energy consumption of the facility, various actual operating values as the various operating values, to calculate an actual calculation value of the energy consumption; (2) dividing the actual value of the energy consumption by the actual calculation value to calculate a reference learning value of the energy consumption; (3) inputting the set operating value defined by the setting calculation unit only in one operating value, among various operating values of the model expression, while inputting the actual operating values collected by the actual value unit in other operating values to calculate a pseudo-actual calculation value of the energy consumption; (4) dividing the actual calculation value by the pseudo-actual calculation value to calculate a correction learning value; and (5) inputting the various set operating values as the various operating values of the model expression to calculate a prediction value of the energy consumption for the material to be rolled, which is scheduled to be conveyed to the rolling line next time or later, and multiplies the prediction value by the reference learning value and the correction learning value to calculate a corrected prediction value of the energy consumption.