An HVAC system includes a compressor, condenser, and evaporator. A sensor measures a value associated with the refrigerant in the condenser or the evaporator, and a controller is communicatively coupled to the compressor and the sensor. The controller determines, based on an operational history the compressor, that pre-requisite criteria are satisfied for entering a sensor validation mode. After determining the pre-requisite criteria are satisfied, an initial sensor measurement value is determined. Following determining the initial sensor measurement value, the compressor is operated according to a sensor-validation mode. Following operating the compressor according to the sensor-validation mode for at least a minimum time, a current sensor measurement value is determined. The controller determines whether validation criteria are satisfied for the current sensor value. In response to determining that the validation criteria are satisfied, the controller determines that the sensor is validated.

An aggregation layer subsystem, and method of operation thereof, for use with an architect subsystem and a plurality of edge processing devices in a distributed analytics system, wherein each edge processing device is adapted to monitor and control the operation of at least one monitored system according to a first analytic model, the aggregation layer subsystem comprising: a processor and memory, the memory containing instructions which, when executed by the processor, enables the aggregation layer subsystem to: receive a second analytic model from the architect subsystem, the second analytic model based on characteristics of at least one monitored system associated with at least one of the plurality of edge processing devices; receive monitored system information from each of the plurality of edge processing devices; and, provide control signals to the at least one monitored system, via one of the edge processing devices, according to the second analytic model in response to the monitored system information.

Training data sets which are obtained by controlling the machine by different control systems are read in, the training data sets each including a state data set and an action data set. Furthermore, a performance evaluator is provided and determines, for a control agent, a performance for controlling the machine by the control agent. A control-system-specific control agent for the different control systems is respectively trained to reproduce an action data set on the basis of a state data set. In addition, a respective environment is delimited on the basis of a distance dimension in a parameter space of the control-system-specific control agents. Test control agents, for each of which a performance value is determined by the performance evaluator, are then generated within the environments. Depending on the determined performance values, a performance-optimizing control agent is finally selected from the test control agents and is used to control the machine.

A replacement part for replacing an original mechanical machine part having has an original mechanical configuration with original part descriptive data is made by first receiving performance data obtained by monitoring the machine during operation with the original machine part with one or more sensors and then sending the performance data to a modeling server. The modeling server then calculates multiple optimized mechanical configurations of the replacement part with one or more modeling algorithms based on different optimization criteria using at least the original part descriptive data and the received performance data. Then a selection of several performance options representing the multiple mechanical configurations of the replacement part are provided, one of which is selected by a user. Finally a replacement part is made with the final optimized configuration corresponding to the selected performance option or sending optimized part descriptive or construction data with the final optimized configuration.

Provided are a system and method for implementing control systems. One example includes configuring a processor to predict instability in control of a system by using multiple non-eigenvalue indices. Instability predictions may be communicated to an actuator of a device being controlled to regulate activity of the device. One example includes using transformation allergic indices (TAIs) as non-eigenvalue indices. One example includes using stability definite indices (SDIs) as novel introduced non-eigenvalue indices.

This invention provides an energy storage device manager, a system comprising the energy storage device manager, computer-readable media configured for providing the energy storage device manager, and methods of using the energy storage device manager. The energy storage device manager can optionally control charge buses and/or load buses to modulate the state of charge of an energy storage device. The energy storage device manager can optionally be configured with a plurality of modes that target different states of charge. The plurality of modes can optionally comprise a maintain mode which targets a nominal (e.g. 50%) charge state and a high-charge mode that targets a state of charge greater than the maintain mode. The plurality of modes can optionally further include an in-use mode which targets a state of charge greater than the maintain mode, and turns on a load bus that is turned off in the preparation mode. The energy storage device manager can optionally be configured to determine a charge start time to execute the preparation mode. The energy storage device manager can optionally be configured to determine the charge start time based on forecast data (e.g. power prediction forecast determined based on weather forecast).

A building management system (BMS) includes sensors that measure time series values of building variables and a deterministic model generator that uses historical values for the time series of building variables to train a deterministic model that predicts deterministic values for the time series. The BMS includes a stochastic model generator that uses differences between actual values for the time series and the predicted deterministic values to train a stochastic model that predicts a stochastic value for the time series. The BMS includes a forecast adjuster that adjusts the predicted deterministic values using the predicted stochastic value to generate an adjusted forecast for the time series. The BMS includes a demand response optimizer that uses the adjusted forecast to generate an optimal set of control actions for building equipment of the BMS. The building equipment operate to affect the building variables.

A heating, ventilation, and air conditioning (HVAC) control device configured to receive a user input for controlling an HVAC system, to determine whether the user input indicates an energy saving occupancy setting, and to identify a first plurality of time entries that are associated with a confidence level for a predicted occupancy status that is less than a predetermined threshold value in the predicted occupancy schedule. The device is further configured to modify the predicted occupancy schedule by setting the first plurality of time entries to an away status when the user input indicates an aggressive energy saving occupancy setting. The device is further configured to modify the predicted occupancy schedule by setting the second plurality of time entries to a present status when the user input indicates a conservative energy saving occupancy setting. The device is further configured to output the modified predicted occupancy schedule.

A method of adaptive persistence forecasting includes receiving historical load values for a site with at least one component that consumes energy; receiving historical temperature values corresponding to dates of the historical load values; evaluating the historical load values and the historical temperature values to determine a correlation coefficient; determining that there exists at least a threshold correlation between a load activity and temperature for the historical load values and the historical temperature values based on the correlation coefficient; in response to determining that there exists at least the threshold correlation, normalizing the historical load values based on a set temperature; and applying an adaptive seasonal persistence model to the normalized historical load values to output a forecast for use in controlling energy resources at the site.

In accordance with a program, a processor obtains a plurality of manufacturing parameters and a measured value of an at least one property of an alloy material, calculates a pre-predicted value based on a first manufacturing parameter included in the plurality of manufacturing parameters using a prediction expression describing a relationship between the first manufacturing parameter, and a pre-predicted value of the property representing a roughly calculated value of a target predicted value that is a target value of the property, calculates a difference between the pre-predicted value and a measured value of the property, and trains a model using a training data set including a second manufacturing parameter and the difference, to generate a trained model that is used to predict the at least one property.