An agricultural harvesting system includes a control system. The control system identifies a predictive value of a power characteristic based on a relationship between the power characteristic and a characteristic. The control system generates a control signal to control a controllable subsystem of a mobile agricultural harvesting machine based on the predictive value of the power characteristic.

A method for controlling a compressed air or gas system is disclosed including the steps of estimating a current state, predicting a future process variable profile based on the current state, sampling the future process variable profile by a sampling method having sampling frequencies based on a volume of the compressed air or gas system, transforming by a model predictive control, MPC, method the sampled future process variable profile and the current state into an action profile and a state profile, and instructing the compressors to perform the actions in accordance with the action profile thereby controlling the compressed air or gas system.

A control system includes a target device configured to be controlled based on a control signal, a sensor configured to measure a physical quantity of the target device, and a control device configured to output the control signal to the target device based on the physical quantity and a command value, and perform feedback control. The control device includes a command value generator configured to generate a command value for the target device, a command speed arithmetic unit configured to calculate a command speed by model predictive control using a dynamic characteristic model indicating a relationship between the control signal and the physical quantity, the command value, and the physical quantity, and a control signal generator configured to generate the control signal by speed control with dead time compensation using a model of the target device, dead time, the command speed, and the physical quantity, and output the control signal to the target device.

A method includes automatically selecting a prediction horizon used by the predictive model by performing evaluations of model performance at successively narrower ranges of possible prediction horizons until the prediction horizon is determined based on results of the evaluations. The method may also include using the predictive model with the prediction horizon to perform an automated control action, which may include at least one of controlling or monitoring the building equipment.

A system includes a plurality of devices of building equipment, an additional device of building equipment, and a computing system. The computing system is configured to process data from the plurality of devices to extract common features of the plurality of devices, train a global model based on the common features, obtain additional data from the additional device, adapt the global model for the additional device based on the additional data to obtain an adapted model for the additional device, predict a status of the additional device using the adapted model, and affect an operation of the additional device based on the status.

A method includes obtaining a fault prediction model for building equipment, predicting, with the fault prediction model, both (i) whether a fault will occur during a first prediction bin and (ii) whether a fault will occur during a second prediction bin, performing a first mitigating action for the building equipment if the fault is predicted to occur during the first prediction bin, and performing a second mitigating action for the building equipment if the fault is predicted to occur during the second prediction bin.

A controller for equipment that operate to provide heating or cooling to a building or campus includes a processing circuit configured to obtain utility rate data indicating a price of resources consumed by the equipment to serve energy loads of the building or campus, obtain an objective function that expresses a total monetary cost of operating the equipment over an optimization period as a function of the utility rate data and an amount of the resources consumed by the equipment, determine a relationship between resource consumption and load production of the equipment, optimize the objective function over the optimization subject to a constraint based on the relationship between the resource consumption and the load production of the equipment to determine a distribution of the load production across the equipment, and operate the equipment to achieve the distribution.

A method for initiating and automatically improving model-driven operations in a low-data scenario includes creating a regression model using pre-operation data prior to initiating the model-driven operations, using the regression model to initiate and perform the model-driven operations during an operational stage, collecting operational data during the operational stage, creating a first artificial neural network model using the operational data, transitioning from using the regression model to perform the model-driven operations to using the first artificial neural network model to perform the model-driven operations responsive to the operational data satisfying a first sufficiency threshold.

A building management system includes one or more processing circuits. The one or more processing circuits are configured to receive, from a physical building device of a building, environmental inputs and environmental outputs of the physical building device; generate a building device digital twin for the physical building device based on the received environmental inputs and the received environmental outputs; generate a predicted future performance of the physical building device based on the building device digital twin; and generate a recommendation based on the predicted future performance of the physical building device, the recommendation indicating one or more changes to implement on the physical building device. The building device digital twin is a model for predicting the behavior of the physical building device ore changes to implement on the physical building device.

The present disclosure describes a sensor system for measuring a process value of a physical system, including: a plurality of sensors, wherein each sensor is configured to generate a sense signal as a function of the process value at a given time; a system state corrector configured to determine an actual system state of the physical system at a given state update cycle; a system state predictor configured to determine a predicted system state of the physical system at a given prediction cycle from a previous system state at a previous state update cycle; a sense signal predictor configured to determine predicted sense signals at the given prediction cycle from the predicted system state by applying a first operation to the predicted system state using a sense signal model of the physical system for predicting the sense signals.