A visualization device includes a processor for carrying out a first displaying process of displaying, on a display device, a first screen indicating power consumption of a whole of an equipment group in each of periods included in a period group, a second displaying process of displaying, on the display device, a second screen indicating a cycle time of each of pieces of equipment included in the equipment group in a particular period, and a third displaying process of displaying, on the display device, a third screen indicating a cycle time of each of processes carried out with use of a particular piece of equipment in the particular period.

The present disclosure describes system and methods for inferring energy usage at multiple levels of granularity. One embodiment describes an industrial automation system including a first industrial automation component, a first sensor coupled to the first industrial automation component, in which the first sensor measures a first amount of power supplied to the first industrial automation component, a second industrial automation component that couples to the first industrial automation component, and an industrial control system that infers energy usage by the first industrial automation component and the second industrial automation component based at least in part on the first amount of power supplied to the first industrial automation component.

The disclosure relates to a method, an electronic device and a computer program product for reducing a carbon dioxide footprint associated with a production process. The carbon dioxide footprint has at least an amount of carbon dioxide emitted during the production process. The method includes the step of obtaining a parameter indicative of a selected cutting feature for production by a cutting tool, the step of obtaining a parameter indicative of a selected work-piece material for production by a cutting tool, the step of determining a set of cutting tools for production based on the obtained parameters, and the step of determining a cutting tool for production from the determined set of cutting tools based on carbon dioxide emission information data associated with each cutting tool in the determined set of cutting tools.

The present disclosure describes system and methods for inferring energy usage at multiple levels of granularity. One embodiment describes an industrial automation system including a first industrial automation component, a first sensor coupled to the first industrial automation component, in which the first sensor measures a first amount of power supplied to the first industrial automation component, a second industrial automation component that couples to the first industrial automation component, and an industrial control system that infers energy usage by the first industrial automation component and the second industrial automation component based at least in part on the first amount of power supplied to the first industrial automation component.

Provided is information to determine whether a maintenance process should be performed on a plant. Provided is an analysis apparatus, including: an operation value analysis unit configured to analyze operation values including a first operation value when a maintenance process is performed on an analysis target plant and a second operation value when the maintenance process is not performed on the analysis target plant, each of the operation values depending on at least any of an amount or quality of deliverables produced or emissions emitted by the analysis target plant under a designated condition; and a value comparison unit configured to compare the first operation value to the second operation value.

A method of manufacturing at least a first product and a second product with at least a first machine and a second machine at minimum cost in an environment in which a cost of energy used by the first machine and the second machine varies as a function of time may include generating multiple chromosomes, determining fitness scores of each of the chromosomes, randomly generating, with probabilities based on the fitness scores, new chromosomes, determining fitness scores of the new chromosomes, selecting one of the new chromosomes with an optimal fitness score, and manufacturing at least the first product and the second product with at least the first machine and the second machine according to a schedule based on the selected new chromosome.

A demand prediction unit predicts a time series of demand values related to a predetermined prediction period using a predictive model. The predictive model is a learned model learned to output a demand value of an energy source by inputting an operation plan value of a plant and a predicted value related to an environment of the plant. An optimizing unit specifies operating indices of a plant that satisfy a plurality of demand values and satisfy a desired condition for each time related to the predicted time series of demand values. A presentation unit presents information related to the time series of operating indices related to the prediction period.

A controller for equipment that operate to provide heating or cooling to a building or campus includes a processing circuit configured to obtain utility rate data indicating a price of resources consumed by the equipment to serve energy loads of the building or campus, obtain an objective function that expresses a total monetary cost of operating the equipment over an optimization period as a function of the utility rate data and an amount of the resources consumed by the equipment, determine a relationship between resource consumption and load production of the equipment, optimize the objective function over the optimization subject to a constraint based on the relationship between the resource consumption and the load production of the equipment to determine a distribution of the load production across the equipment, and operate the equipment to achieve the distribution.

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.

An optimization system for a central plant includes a processing circuit configured to receive load prediction data indicating building energy loads and utility rate data indicating a price of one or more resources consumed by equipment of the central plant to serve the building energy loads. The optimization system includes a high level optimization module configured to generate an objective function that expresses a total monetary cost of operating the central plant over an optimization period as a function of the utility rate data and an amount of the one or more resources consumed by the central plant equipment. The optimization system includes a demand charge module configured to modify the objective function to account for a demand charge indicating a cost associated with maximum power consumption during a demand charge period. The high level optimization module is configured to optimize the objective function over the demand charge period.