A method and a correspondingly configured bending machine are described, wherein a setting of the bending machine is first calculated on the basis of a mathematical model and subsequently corrected on the basis of stored data sets on previous bending operations and their correction values.

The present disclosure presents techniques for improving operational efficiency of climate control systems. A climate control system may include climate control equipment, a sensor that measures temperature in a building, and a control system that controls operation of the equipment using a first temperature schedule, which associates each time step with a temperature setpoint, when the building is occupied. When not occupied, the control system determines an expected return time based on historical occupancy data associated with the building, determines the temperature setpoint associated with the expected return time, determines candidate schedules each expected to result in the inside air temperature meeting the temperature setpoint, determines efficiency metrics each associated with one of the candidates based on historical performance data resulting from previous operation of the climate control system, and controls operation of the equipment based on a second temperature schedule selected from the candidates based on associated efficiency metrics.

A building energy system includes HVAC equipment, green energy generation, a battery, and a predictive controller. The HVAC equipment provide heating or cooling for a building. The green energy generation collect green energy from a green energy source. The battery stores electric energy including at least a portion of the green energy provided by the green energy generation and grid energy purchased from an energy grid and discharges the stored electric energy for use in powering the HVAC equipment. The predictive controller generates a constraint that defines a total energy consumption of the HVAC equipment at each time step of an optimization period as a summation of multiple source-specific energy components and optimizes the predictive cost function subject to the constraint to determine values for each of the source-specific energy components at each time step of the optimization period.

A management system includes a first receiver receiving, at a predetermined time interval, a reference value of a reference power meter, a second receiver receiving, at an interval shorter than the time interval, a first measurement value of a first power meter closer to a power system than a merging point of a power line connected to a distributed power supply and a power line connected to a load device, a third receiver receiving, at an interval shorter than the time interval, a second measurement value of a second power meter for measuring power consumption of the load device, and a controller executing a first calibration process related to the first power meter based on a comparison between the reference value and the first measurement value and subsequently executing a second calibration process related to the second power meter based on the first measurement value and the second measurement value.

Disclosed is a machine learning device of a cutting condition adjustment apparatus including: a state observation section that observes, as state variables indicating a current state of an environment, cutting condition data indicating a laser cutting condition for a laser cutting and oblique rearward temperature rise data indicating a temperature rise value at an oblique rearward part of a cutting front of a workpiece, a determination data acquisition unit that acquires temperature rise value determination data for determining propriety of the temperature rise value during cutting based on the laser cutting condition for the laser cutting as determination data indicating a propriety determination result of the cutting of the workpiece, and a learning unit that learns the temperature rise value and adjustment of the laser cutting condition for the laser cutting in association with each other using the state variables and the determination data.

According to some embodiments, an available algorithm data store may contain information about a pool of available algorithms. An algorithm selection platform coupled to the available algorithm data store may access the information about the pool of available algorithms and compare the information about each of the pool of available algorithms with at least one requirement associated with the current algorithm executing in the real environment. The algorithm selection platform may then automatically determine algorithm execution context information and, based on said comparison and the algorithm execution context information, select at least one of the pool of available algorithms as a potential replacement algorithm. An indication of the selected at least one potential replacement algorithm may then be transmitted (e.g., to be evaluated in a shadow environment by an algorithm evaluation platform).

A heating, ventilation, and/or air conditioning (HVAC) system includes a compressor having an impeller configured to rotate and drive a working fluid through a working fluid circuit of the HVAC system in an active operating mode. The HVAC system further includes a controller configured to, in response to receiving an input indicative of a transition to operate in an inactive operating mode, suspend the active operating mode by interrupting a supply of power to the compressor, adjust pre-rotation vanes of the compressor, a variable geometry diffuser of the compressor to a first position, or both to enable a backflow of the working fluid through the compressor for a first interval of time, and, after the first interval of time has elapsed, adjust the pre-rotation vanes, the variable geometry diffuser, or both to a second position to block the backflow of the working fluid through the compressor.

A feedback control device that takes information regarding a control deviation between a measured value and a desired value of a controlled object as input, and outputs a manipulated variable for the controlled object, includes: a first control unit that takes information regarding the control deviation as input, and outputs a manipulated variable for the controlled object; a second control unit that takes information regarding the control deviation as input, and that includes a learning control unit in which a parameter for outputting a manipulated variable for the controlled object is determined by machine learning; and an adder that adds a first manipulated variable output from the first control unit and a second manipulated variable output from the second control unit. A manipulated variable from the adder is output to the controlled object, and the second control unit includes a limiter that limits the second manipulated variable.

A cooling system for the decoating system includes a sensor, a control device, and a controller communicatively coupled with the sensor and the control device. The sensor is configured to measure a characteristic of the cooling system in the decoating system, the control device controls the characteristic of the cooling system, and the controller is configured to adjust the control device to adjust the characteristic of the cooling system based on at least one of a measured temperature within the decoating system or the measured characteristic. A method of controlling a temperature of the decoating system includes measuring a temperature within a piece of equipment of the decoating system and measuring a characteristic the cooling system in the piece of equipment of the decoating system. The method includes controlling the cooling system to adjust the characteristic based on at least one of the measured temperature or the measured characteristic.

A controller for controlling a system is provided. The controller performs measuring variables via an interface to generate a vector of variables, providing a cost function, with respect to the system, based on the vector variables using weighting factors, wherein the vector variables are represented by a time-step, computing first-derivative of the cost function at an initial time-step, obtaining a convergence time from the first-derivative of the cost function, computing second derivative of the cost function and generating an optimization differential equation based on the first and second derivatives of the cost function, proceeding, starting with the initial time-step, to obtain a value of the optimization differential equation by solving the optimization differential equation, in an iteration manner, with a predetermined time step being multiplied with the value of the solved differential equation to obtain next vector variables corresponding to a next iteration time-step, until the time-step reaches the convergence time, and outputting optimal values of the vector of variables and the cost function.