A system for generating and using a predictive model to control building equipment includes building equipment operable to affect one or more variables in a building and an operating data aggregator that collects a set of operating data for the building equipment. The system includes an autocorrelation corrector that removes an autocorrelated model error from the set of operating data by determining a residual error representing a difference between an actual output of the building equipment and an output predicted by the predictive model, using the residual error to calculate an autocorrelation for the model error, and transforming the set of operating data using the autocorrelation. The system includes a model generator module that generates a set of model coefficients for the predictive model using the transformed set of operating data and a controller that controls the building equipment by executing a model-based control strategy that uses the predictive model.

An automation system includes a first control facility with a first fieldbus connection, a second control facility with a second fieldbus connection, a fieldbus, a peripheral board with at least one I/O peripheral module, wherein the peripheral board has an interface module with a third fieldbus connection, where the interface module has at least one I/O module which stores interconnection information featuring an assignment of input/outputs of the I/O peripheral module(s) to the control facilities, where the interface module additionally has a virtual I/O module, in which an output region is assigned to the first control facility, and furthermore an input region is assigned to the second controller, and where the virtual I/O module is configured such that the output data is copied from the output region of the first control facility into the input region as input data for the second control facility.

Systems and methods are directed controlling components of a utility grid. The system can receive signals. The system can determine one or more statistical metrics based on the signals. The system can generate an input matrix. The system can input the input matrix into a machine learning model. The system can predict, based on the input matrix and via the machine learning model, the value for the signal of the utility grid at a time period for which the value is not provided in the input matrix. The system can provide a command to control a component of the utility grid responsive to the value for the signal of the utility grid predicted by the machine learning model.

Systems and methods are directed controlling components of a utility grid. The system can receive signals. The system can determine one or more statistical metrics based on the signals. The system can generate an input matrix. The system can input the input matrix into a machine learning model. The system can predict, based on the input matrix and via the machine learning model, the value for the signal of the utility grid at a time period for which the value is not provided in the input matrix. The system can provide a command to control a component of the utility grid responsive to the value for the signal of the utility grid predicted by the machine learning model.

The present invention discloses a programmable controlled intelligent cooking machine which has an electrical control device and an automatic ingredient feeding device connected with the electrical control device; the automatic ingredient feeding device is adapted for receiving a standard package box with a plurality of compartments stocked with ingredients; the electrical control device is adapted for receiving preset recipe command and sending corresponding control command according to the recipe command; the automatic ingredient feeding device is adapted for providing major ingredients and accessory ingredients stocked in respective compartments of the standard package box according to a preset feeding sequence in the received corresponding control command; the combinations of feeding sequence for each Chinese dish can change. The present invention further discloses a feeding and cooking control method of a programmable controlled intelligent cooking machine.

A method for determining a physical model of an energy installation from a plurality of components linked together according to one or more constraints to form a tree, called tree of constraints, each component including one or more output ports, each output port being associated with a physical quantity of which the value depends on one or more variables internal to the component and/or on one or more variables external to the component, each external variable being communicated to the component through an input port. A second aspect relates to a method for controlling an electrical installation including a first phase of determining a physical model of the installation using the described method; and a second control phase during which each set point is determined as a function of a simulation carried out using the physical model obtained during the phase of determining a physical model of the energy installation.

A method for determining a physical model of an energy installation from a plurality of components linked together according to one or more constraints to form a tree, called tree of constraints, each component including one or more output ports, each output port being associated with a physical quantity of which the value depends on one or more variables internal to the component and/or on one or more variables external to the component, each external variable being communicated to the component through an input port. A second aspect relates to a method for controlling an electrical installation including a first phase of determining a physical model of the installation using the described method; and a second control phase during which each set point is determined as a function of a simulation carried out using the physical model obtained during the phase of determining a physical model of the energy installation.

A method includes: receiving a mathematical model of a control system, with state variables and control parameters; discretizing at least a part of a space to obtain a set of tuples; determining for each tuple at least one successor state; obtaining an initial winning set of tuples; determining an updated winning set of tuples, including comparing the at least one successor state with the initial winning set of tuples, where the comparison is distributed over available processing elements by choosing one processing element from the available processing elements for each tuple to perform the comparison and where the available processing elements are used simultaneously at least in part; repeating the determination of the updated winning set of tuples to obtain a new updated winning set of tuples if a convergence measure does not meet a criterion, and constructing a controller for the control system from the new updated winning set.

User comfort control system having non-invasive bio-patch. In some embodiments, a system for providing comfort of a person can include a wearable patch configured to be attached to a skin of the person and sense a biological condition of the skin, and to transmit information representative of the sensed biological condition. The system can further include a controller configured to receive the information and generate a control signal. The system can further include an adjustment element associated with a furniture item implemented to support and provide comfort for the person. The adjustment element can be in communication with the controller and be configured to adjust a comfort level of the furniture item for the person in response to the control signal.

User comfort control system having non-invasive bio-patch. In some embodiments, a system for providing comfort of a person can include a wearable patch configured to be attached to a skin of the person and sense a biological condition of the skin, and to transmit information representative of the sensed biological condition. The system can further include a controller configured to receive the information and generate a control signal. The system can further include an adjustment element associated with a furniture item implemented to support and provide comfort for the person. The adjustment element can be in communication with the controller and be configured to adjust a comfort level of the furniture item for the person in response to the control signal.