A device, computer program, and computer-implemented method for determining a variable of a technical system. An input variable is determined for a first model for determining the variable at a first temporal resolution. A first time series is provided, at the first temporal resolution, including values which characterize an operating variable of the technical system. A second time series is provided. at a second temporal resolution, including values which characterize the operating variable of the technical system, the first and second temporal resolutions being different. The second time series is mapped using a second model for determining a first prediction for the variable of the technical system at the second temporal resolution on the first prediction. Parameters of a second model are determined, using the second time series, which are mapped on parameters of a third model at the first temporal resolution.

A method for controlling production of one or more refined resources by an energy provider includes predicting a demand for the refined resources by one or more consumers of the refined resources as a function of an incentive offered by the energy provider. The method further includes performing an optimization of an objective function subject to a constraint based on the predicted demand for the refined resources to determine an amount of the refined resources for the energy provider to produce and a value of the incentive at multiple times within a time period. The method also includes providing setpoints for equipment of the energy provider that cause the equipment to produce the amount of the refined resources determined by performing the optimization.

Methods and systems are described for a computer-implemented complex adaptive systems metrology (CASM) technique for generating universally and mathematically standardized scores that quantify longitudinal evidence for either temporal-interaction scores or temporal-interaction benefit-and-harm scores to determine a quantitative significance estimate of scores for either standardized temporal-interaction scores or the standardized temporal-interaction benefit-and-harm scores.

Subject matter disclosed herein may relate to time-series mixing for adaptive system training and may relate more particularly to causality-preserving time series mixing for adaptive system training.

A building system including one or more memory devices having instructions stored thereon, that, when executed by one or more processors, cause the one or more processors to receive an event from an event source, the event comprising data and a timestamp. The building system operates to identify first contextual data describing the event in a digital twin, the digital twin comprising a virtual representation of a building, enrich the event with the first contextual data, and provide the enriched event to a consuming system, the consuming system generating an output event based on the enriched event. The building system operates to identify second contextual data describing the output event in the digital twin, enrich the output event with the second contextual data, and provide the enriched output event to the consuming system or another consuming system.

Provided is a source-load cooperation access method for a power distribution region. The method includes: establishing a timing feature model of a distributed generator and a timing feature model of a load respectively, acquiring a timing feature of the distributed generator in an access power distribution region by using maximum likelihood estimation and acquiring a timing feature of a user which accesses the power distribution region by using classification and regression trees; and inputting the timing feature of the distributed generator and the timing feature of the user which access the power distribution region into a combination optimization model, and determining a source-load access feeder through optimization.

The present disclosure provides a method and an apparatus for adjusting a process control prediction model, and a process controller. In an embodiment, the method includes: determining, based on controlled variable data in process control data obtained through real-time monitoring, whether a prediction performance of the process control prediction model is lower than a reference performance; and when the prediction performance is determined to be lower than the reference performance, using manipulated variable data in the process control data monitored to adjust the process control prediction model. By way of the method, a re-test does not need to be executed to re-identify a model so as to eliminate a mismatch of the process control prediction model, thereby eliminating an influence of fluctuation introduced by addition of an excitation signal during the re-testing.

Embodiments include a computer-implemented method (and system) for performing automated batch data alignment for modeling, monitoring, and control of an industrial batch process. The method (and system) loads, scales, and screens plant historian batch data for an industrial batch process. The method (and system) selects a reference batch as basis of the batch alignment, defines and adds or modifies one or more batch phases, and selects one or more batch variables based on one or more profiles and corresponding curvatures of the batch data. The method (and system) estimates one or more weightings, adjust one or more tuning parameters and uses a sliding time window combined with DTW, DTI and GSS algorithms, performs the batch alignment in offline mode or online mode.

A building system including one or more memory devices having instructions stored thereon, that, when executed by one or more processors, cause the one or more processors to generate an event subscription for a consuming system, the event subscription defining events to be sent to the consuming system. The building system operates to receive an event from an event source, the event comprising data and a timestamp and identify contextual data describing the event in a digital twin, the digital twin comprising a virtual representation of a building. The building system operates to enrich the event with the contextual data and provide, based on the event subscription and the contextual data of the enriched event, the enriched event to the consuming system.

Provided is an aluminum oxide production operation optimization system and method based on a cloud-edge collaboration, which relates to the technical field of an aluminum oxide production operation optimization. According to the system and method, firstly the whole-flow data in the aluminum oxide production process is acquired, the data is pre-processed, then the pre-processed data is transmitted to a local collaboration production operation optimization unit, the local collaboration production operation optimization unit firstly judges working conditions for the current aluminum oxide production process, an optimization strategy needing to be operated at present is automatically switched according to the working condition, and the local operation optimization strategy obtains the actual setting value of the aluminum oxide production operation indexes.