A system and method of simulating and optimizing industrial and other processes includes a computer that performs multivariate analysis of input variables and output variables to generate a data model of the operation of the process. For industrial applications, the input variables include process variables and the output variables include result variables from the operation of the industrial process. The data model determines contributions to changes in the output or result variables by the respective input or process variables and is provided to a predictive algorithm to identify parameter values for input or process variables expected to have a most significant impact on the output or result variables during performance of the process. The outputs of the predictive algorithm are parameter values that are provided as input or process variables to the industrial process for simulation or performance optimization or product recommendations/optimizations.

A method of generating a controller for a continuous process. The method includes receiving from a storage memory, off-line stored values of one or more controlled variables and one or more manipulated variables of the continuous process over a plurality of time points. The off-line stored values are used to train a first neural network to operate as a predictor of the controlled variables. Then, the method includes training a second neural network to operate as a controller of the continuous process using the first neural network after it was trained to operate as the predictor for the continuous process and employing the second neural network as a controller of the continuous process.

There is provided a comfort management system including a networked comfort management control device. The comfort management control device operates an HVAC interface to maintain an environment utilizing a determined comfort zone range for one or more occupants of an area treated by the HVAC system, and utilizes controlled deviations from an initial set point to maintain comfort while maximizing energy efficiency of the HVAC system.

A model learning unit learns a prediction model on the basis of learning data, a target setting unit sets a target output parameter value by interpolating between a goal output parameter value and an output parameter value which is the closest to the goal output parameter value in output parameter values in the learning data, a processing condition search unit estimates input parameter values which corresponds to the goal output parameter value and the target output parameter value, a model learning unit updates the prediction model by using a set of the estimated input parameter value and an output parameter value which is a result of processing that a processing device performs as additional learning data.

Provided is a control device for performing model prediction control. A position of a virtual obstacle associated with a real obstacle is set based on a position of the real obstacle acquired by a first acquisition part so that the virtual obstacle is positioned substantially symmetrically to the real obstacle with reference to a following target trajectory of a control target. Stage costs calculated by a prescribed evaluation function include: a first stage cost associated with a first probability potential field representing a probability that the real obstacle is present based on the position of the real obstacle; and a second stage cost associated with a second probability potential field representing a probability that the virtual obstacle is present based on the position of the virtual obstacle and having a probability value equal to or greater than that of the first probability potential field.

A system for separating documents in a batch of unseparated documents. In one example, the system comprises a scanner, a display, and an electronic processor. In another example, the system comprises an electronic source, a display, and an electronic processor. The electronic processor is configured to receive, as input, a batch of unseparated documents and apply, image processing to each page in the batch. The electronic processor is also configured to determine, for each pair of consecutive pages in the batch of documents, a probability that pages of the pair of consecutive pages belong to different documents using a predictive model. The electronic processor is further configured to generate a batch of separated documents by providing an indication of a document boundary if the probability generated by the predictive model is above a predetermined threshold.

A system for intelligent monitoring and management of an electrical system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics server is comprised of a real-time energy pricing engine, virtual system modeling engine, an analytics engine, a machine learning engine and a schematic user interface creator engine. The real-time energy pricing engine generates real-time utility power pricing data. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-time data output and predicted data output of the electrical system. The machine learning engine stores and processes patterns observed from the real-time data output and the predicted data output to forecast an aspect of the electrical system.

To infer dynamic control information on a controlled object. An inferring device includes one or more memories and one or more processors. The one or more processors are configured to: input at least data about a state of a controlled object and time-series control information for controlling the controlled object, into a network trained by machine learning; acquire predicted data about a future state of the controlled object controlled based on the time-series control information via the network into which the data about the state of the controlled object and the time-series control information have been input; and output new time-series control information for controlling the controlled object to bring the future state of the controlled object into a target state based on the predicted data acquired via the network.

Provided is a prediction device that outputs a prediction value of process data in consideration of a prediction error of a prediction model. A prediction device includes a data collection unit that collects process data of a device; a prediction model construction unit that constructs a prediction model having a predetermined input variable of first process data as an input value and having a predetermined output variable as an output value, and an error calculation model which calculates a prediction error of the prediction model, based on the first process data collected by the data collection unit; and a prediction unit that outputs a prediction value which is corrected based on a prediction value of the output variable for second process data and a prediction error for the prediction value of the output variable, the prediction value being predicted based on the input variable of the second process data collected by the data collection unit, the prediction model, and the error calculation model.

A system and method for energy optimization is disclosed. The system may collect information from an information collector data including energy usage and storage data of at least one renewable energy generation system and battery energy storage system (BESS). The system may identify historical events that result in curtailment of renewable energy production, determine whether there is a curtailment of renewable energy production based at least on one historical event supervise the charge and discharge cycles of the at least one BESS; and ensuring that the diesel generators minimum up/down time is satisfied based on controlling at least one parameter of the BESS.