One example includes a forecast engine that generates forecast data that characterizes predicted operating conditions of an energy storage system for a given time period in the future, wherein the predicted operating conditions are based on a load history for a power consuming premises coupled to the energy storage system and on a value history for power provided to and consumed from a power grid. The load history of the power consuming premises characterizes unmetered power transferred to the power consuming premises, metered powered transferred from the power grid to the power consuming premises and metered powered exchanged from the energy storage system to the power grid. In the example, a schedule manager generates an operation schedule for operating the energy storage system. The operation schedule includes charge and discharge patterns for an energy storage source that are tuned to curtail power costs and/or elevate power revenue value.

Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to the operation of assets. In particular, examples involve defining and deploying aggregate, predictive models and corresponding workflows, defining and deploying individualized, predictive models and/or corresponding workflows, and dynamically adjusting the execution of model-workflow pairs.

A ceiling-mounted sensing unit includes (i) one or more air temperature sensors; (ii) an infrared sensor having a field of view oriented towards a floor of the room; and (iii) a microcontroller receiving readings from both the air temperature sensors and the infrared sensor, the microcontroller providing an estimated temperature at a predetermined distance above the floor of the room based on a model of the room. The model may be based on a double-exponential smoothing function obtained by matching a Kalman filter model. Alternately, the model may be itself a Kalman filter model or a machine learning trained linear model obtained using a linear regression technique, such as L2 regularization. The Kalman filter model uses a state vector that includes both the estimated temperature and a rate of change in the estimated change in temperature. The machine-trained model may be verified using a k-fold cross-validation technique.

Disclosed herein is an artificial intelligence device. An an artificial intelligence device according to an embodiment of the present invention includes a communication unit that obtains an external environmental factor and an internal environmental factor collected by a sensor and a processor that provides the internal environmental factors to an environmental factor prediction model to predict a subsequent internal environmental factor and allows a ventilation system and an air cleaning system to operate cooperatively with each other based on the predicted internal environmental factor.

An air-conditioning control method includes: acquiring a first sensor value measured by a first sensor device provided at a different position within an identical space to a second sensor device at a first frequency; acquiring a second sensor value measured by the second sensor device at a second frequency, wherein the second frequency is higher than the first frequency; generating a first sensor predicted value from the second sensor value based on a correlation between the first sensor value and the second sensor value in a period in which the second sensor value is acquired and the first sensor value is not acquired; and determining an operation of an air-conditioning apparatus based on the first sensor predicted value.

A control system for causing an output of a control target to follow a command includes: a first processing device which is a processing device having a first processor and a prediction model that defines a correlation between a state variable with respect to the predetermined control target and a control input to the predetermined control target in the form of a state equation, performs model predictive control using the first processor, and outputs a servo command corresponding to the control input at an initial time point of the prediction interval; and a second processing device which is a processing device having a second processor different from the first processor and a feedback system including controllers to which a feedback signal related to an operation of the predetermined control target is input and receiving the servo command from the first processing device, and performs feedback control using the second processor.

A method of operating a computer device includes obtaining measurements of one or more conditions of a power grid component associated with the computer device. The computer device forecasts a future state of the one or more conditions of the power grid component associated with the computer device. The computer device communicates with other computer devices associated with other power grid components to negotiate a behavior of the power grid component associated with the computer device using the forecast.

A model predictive maintenance system for building equipment including an equipment controller to operate the building equipment to affect a variable state or condition in a building. The system includes an operational cost predictor to predict a cost of operating the building equipment over a duration of an optimization period, a maintenance cost predictor to predict a cost of performing maintenance on the building equipment, and a cost incentive manager to determine whether any cost incentives are available and, in response to a determination that cost incentives are available, identify the cost incentives. The system includes an objective function optimizer to optimize an objective function to predict a total cost associated with the building equipment over the duration of the optimization period. The objective function includes the predicted cost of operating the building equipment, the predicted cost of performing maintenance on the building equipment, and, if available, the cost incentives.

A control device (70) of a plant which controls operation amounts of a plurality of types of equipment constituting the plant, the control device includes a prediction model deriving unit (71) that derives a prediction model from which a monitoring target value is output, an adjustment amount calculating unit (72) that calculates adjustment amounts of operation amounts in the case where the monitoring target value becomes a desired value and calculates differences between the operation amounts of existing control and the calculated operation amounts as first adjustment amounts, a constraint condition setting unit (73) that sets constraint conditions based on a measurement value and operation conditions of the plant 1, and an operation amount calculating unit (74) that calculates second adjustment amounts to which the constraint conditions are applied and calculates a plurality of the adjusted operation amounts based on each of the calculated second adjustment amounts.

Systems and methods for controlling device performance variability during manufacturing of a device on wafers are disclosed. The system includes a process platform, on-board metrology (OBM) tools, and a first server that stores a machine-learning based process control model. The first server combines virtual metrology (VM) data and OBM data to predict a spatial distribution of one or more dimensions of interest on a wafer. The system further comprises an in-line metrology tool, such as SEM, to measure the one or more dimensions of interest on a subset of wafers sampled from each lot. A second server having a machine-learning engine receives from the first server the predicted spatial distribution of the one or more dimensions of interest based on VM and OBM, and also receives SEM metrology data, and updates the process control model periodically (e.g., wafer-to-wafer, lot-to-lot, chamber-to-chamber etc.) using machine learning techniques.