A system for controlling heating, ventilation, or air conditioning (HVAC) equipment of a building includes one or more processing circuits configured to generate simulated building data using a simulation model of the building, pre-train a reinforcement learning (RL) model using the simulated building data, operate the HVAC equipment of the building using the RL model, and retrain the RL model using actual building data generated responsive to operating the HVAC equipment using the RL model.

Deep Learning is a candidate for advanced process control, but requires a significant amount of process data not normally available from regular plant operation data. Embodiments disclosed herein are directed to solving this issue. One example embodiment is a method for creating a Deep Learning based model predictive controller for an industrial process. The example method includes creating a linear dynamic model of the industrial process, and based on the linear dynamic model, creating a linear model predictive controller to control and perturb the industrial process. The linear model predictive controller is employed in the industrial process and data is collected during execution of the industrial process. The example method further includes training a Deep Learning model of the industrial process based on the data collected using the linear model predictive controller, and based on the Deep Learning model, creating a Deep Learning model predictive controller to control the industrial process.

A priori georeferenced vegetative index data is obtained for a worksite, along with field data that is collected by a sensor on a work machine that is performing an operation at the worksite. A predictive model is generated, while the machine is performing the operation, based on the georeferenced vegetative index data and the field data. A model quality metric is generated for the predictive model and is used to determine whether the predictive model is a qualified predicative model. If so, a control system controls a subsystem of the work machine, using the qualified predictive model, and a position of the work machine, to perform the operation.

An environmental control system of a building including a first building device operable to affect environmental conditions of a zone of the building by providing a first input to the zone. The system includes a second building device operable to independently affect a subset of the environmental conditions by providing a second input to the zone and further includes a controller including a processing circuit. The processing circuit is configured to perform an optimization to generate control decisions for the building devices. The optimization is performed subject to constraints for the environmental conditions and uses a predictive model that predicts an effect of the control decisions on the environmental conditions. The processing circuit is configured to operate the building devices in accordance with the control decisions.

A material characteristic value prediction system that can predict material characteristic values with high accuracy is provided. Also provided is a method of manufacturing a metal sheet that can improve the product yield rate, by changing manufacturing conditions of subsequent processes. The material characteristic value prediction system (100) includes a material characteristic value predictor configured to acquire input data including line output factors in a metal sheet manufacturing line, disturbance factors, and component values of a metal sheet being manufactured, and predict material characteristic values of the manufactured metal sheet using a prediction model configured to take the input data as inputs, wherein the prediction model includes a machine learning model generated by machine learning and configured to take the input data as inputs and output production condition factors, and a metallurgical model configured to take the production condition factors as inputs and output the material characteristic values.

A building system includes one or more storage devices having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to receive an unstructured user question from a user device of a user and query a graph database based on the unstructured user question to extract context associated with the unstructured user question from contextual information of a building stored by the graph database, wherein the graph database stores the contextual information of the building through nodes and edges between the nodes, wherein the nodes represent equipment, spaces, people, and events associated building and the edges represent relationships between the equipment, spaces, people, and events. The instructions further cause the one or more processors to retrieve data from one or more data sources based on the context and compose a presentation based on the retrieved data.

A predictive analytics apparatus, engine, system and method capable of providing real time analytics in a manufacturing system. The apparatus, engine, system and method may include a data input capable of receiving raw data output from at least one machine operable to effect the manufacturing system embodiments, and a processor associated with a computing memory and suitable for executing code from the computing memory. The code may include an adaptor capable of pushing the received raw data to one or more databases to processed data; an extractor capable of extracting the processed data from the one or more databases; predictive analytics capable of receiving the extracted processed data and applying thereto at least one predictive model including target data for the at least one machine, and capable of providing feedback to the at least one machine to modify performance of the at least one machine based on the application of the at least one predictive model; and a visualizer capable of providing at least a visualization of the feedback and of the performance.

A quality prediction device includes a probabilistic prediction model generation unit that generates a probabilistic prediction model on the basis of a plurality of training data in which a log relating to a molding operating condition or to a state of a molding machine and a quality value of a molding product corresponding to the log are associated with each other, and a quality prediction unit that calculates a quality index of the molding product from the log using the probabilistic prediction model.

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 hybrid agricultural harvesting machine based on the predictive value of the power characteristic.

A system for product replacement includes a product logistics system for a product in a product condition. The system includes an exposure data collection system configured to collect exposure data indicating at least one of an event or an environmental condition that capable of impacting the product condition of the product. The system includes a replacement determination system programmed to calculate a probability for the need to replace the product based on the at least one of the event or the environmental condition. The system includes a replacement procurement system programmed to autonomously configure an option-type futures contract for replacement of the product based on the probability for the need to replace the product.