A method for performing validation, estimation, and editing (VEE), including: displaying real-time electrical usage for a building on a controllable video display; executing VEE rules on each of the streams to generate and store a corresponding post VEE readings, the post VEE readings comprising tagged energy consumption data sets each associated with a corresponding one of the streams, each of the data sets comprising first groups of contiguous interval values tagged as having been validated and second groups of contiguous interval values tagged as having been edited, the first groups of contiguous interval values corresponding to correct data; for the each of the data sets, creating anomalies having different durations using only the first groups of contiguous interval values; generating estimates for the anomalies by employing estimation techniques; for each of the durations, selecting one of the estimation techniques for subsequent employment when performing VEE of subsequent energy consumption data for the corresponding one of the streams; and executing functions on the streams translated by the generating and directing directing the controllable video display to display a weather normalized usage baseline recommendation for action regarding current energy usage.

An apparatus for configuring validation, estimation, and editing (VEE) rules for performing VEE on interval-based energy consumption streams includes a data stores, a rules processor, and a process control element. The stores provides tagged data sets each associated with a corresponding one of the streams, each of the sets comprising groups of contiguous interval values tagged as having been validated. The rules processor reads the stores upon initiation of an event and, for the each of the sets, creates anomalies having different durations using only the groups of values, the groups of values corresponding to correct data, and generates estimates for the anomalies by employing estimation techniques for each of the durations and, for the each of the durations, selects a corresponding one of the techniques for subsequent employment when performing VEE of subsequent energy consumption data associated with the each of the durations for the corresponding one of the streams. The control element executes functions on the streams translated by the rules processor, and directs one or more system elements to change state.

A method for performing validation, estimation, and editing (VEE), including: employing HVAC subsystems to adjust comfort in a building; performing VEE on energy consumption streams; executing VEE rules on each of the streams to generate and store a corresponding post VEE readings, the post VEE readings comprising tagged energy consumption data sets each associated with a corresponding one of the streams, each of the data sets comprising groups of contiguous interval values tagged corresponding to correct data; for the each of the data sets, creating anomalies having different durations using only the first groups of contiguous interval values; generating estimates for the anomalies by employing estimation techniques; for each of the durations, selecting one of the estimation techniques for subsequent employment when performing VEE of subsequent energy consumption data for the corresponding one of the streams; and executing functions on the streams translated by the generating and directing one or more of the HVAC subsystems to change state, where the comfort is adjusted in accordance with building energy consumption.

A method for performing validation, estimation, and editing (VEE), including: employing HVAC subsystems to adjust comfort in a building; performing VEE on energy consumption streams; executing VEE rules on each of the streams to generate and store a corresponding post VEE readings, the post VEE readings comprising tagged energy consumption data sets each associated with a corresponding one of the streams, each of the data sets comprising first groups of contiguous interval values tagged as having been validated and second groups of contiguous interval values tagged as having been edited, the first groups of contiguous interval values corresponding to correct data; for the each of the data sets, creating anomalies having different durations using only the first groups of contiguous interval values; generating estimates for the anomalies by employing estimation techniques; for each of the durations, selecting one of the estimation techniques for subsequent employment when performing VEE of subsequent energy consumption data for the corresponding one of the streams; and executing functions on the streams translated by the generating and directing one or more of the HVAC subsystems to change state, where the comfort is adjusted in accordance with building energy consumption.

A device includes a catalog storage unit that stores a catalog having versatility; a catalog control unit that specifies the catalog corresponding to a target machine tool to be subjected to state determination based on machine tool information including an item of facility type and acquires the catalog from the catalog storage unit; a feature quantity extraction unit that extracts a feature quantity from sensor data detected from the target machine tool; a sensor data processing unit that performs state determination of the target machine tool based on the feature quantity distribution included in the acquired catalog and the feature quantity extracted from the sensor data; a feature quantity tuning unit that performs tuning of the feature quantity distribution by mapping the extracted feature quantity to the feature quantity distribution; and a catalog updating unit that updates the catalog based on the feature quantity distribution after tuning.

A method for performing validation, estimation, and editing (VEE), including: displaying real-time electrical usage for a building on a controllable video display; executing VEE rules on each of the streams to generate and store a corresponding post VEE readings, the post VEE readings comprising tagged energy consumption data sets each associated with a corresponding one of the streams, each of the data sets comprising groups of contiguous interval values tagged as having been validated and corresponding to correct data; for the each of the data sets, creating anomalies having different durations using only the groups of contiguous interval values; generating estimates for the anomalies by employing estimation techniques; for each of the durations, selecting one of the estimation techniques for subsequent employment when performing VEE of subsequent energy consumption data for the corresponding one of the streams; and executing functions on the streams translated by the generating and directing the controllable video display to display a weather normalized usage baseline recommendation for action regarding current energy usage.

The present disclosure includes a method for monitoring an automation system having a multiplicity of field devices arranged in a spatially distributed fashion and configured to acquire or set a primary process variable that is dependent on the process. The field devices are also configured to each acquire at least one secondary environmental variable and have at least one superordinate process control unit for controlling the process. The method includes transmitting the acquired environmental variables to a superordinate database and analysis platform and monitoring the automation system using the database and analysis platform. To monitor the automation system, the database and analysis platform compares the acquired transmitted secondary environmental variables with comparative environmental variables which represent a usual state of the automation system, in order to detect an unusual state in the automation system.

A computing system for receiving operational data including process parameters generated by sensors in a plant. An analysis engine uses the operational data to automatically provide a first listing of worst performing process parameters, that when a selected poor performing process parameter is chosen generates a ranked filtered view of equipment parameters for associated processing equipment that may be affected by the selected poor performing parameter and a filtered view of recommendations for recognizing action(s) to fix the associated processing equipment and/or the selected poor performing process parameter, and/or a second listing of worst performing processing equipment that when a selected poor performing processing equipment is chosen generates a ranked filtered view of suspected process parameters that may be affected by the selected poor performing processing equipment along with a filtered view of recommendations for recognizing action(s) to fix the selected poor performing processing equipment and the suspected process parameters.

A system for analyzing operational data associated with a plant that has processing equipment configured and controlled to run a process involving at least one tangible material. Actual operational data corresponding to plant operations is received by a computing device that may relate to production, equipment, workforce, automation systems, safety, and/or cybersecurity of the plant. A model of the plant is generated based on the actual operational data, where the model indicates ideal plant operations including model operational data. The actual plant operational data and the model operational data are compared. Based on the operational data and the comparison of the operational data to model operational data, at least one recommendation for an action associated with the plant is determined.

A system may include an industrial automation device, a computing device configured to receive a first set of data associated with the industrial automation device, and a cloud-computing system. The cloud-computing system may receive the first set of data from the computing device, identify a second set of data associated with the industrial automation device based on the first set of data, and send the second set of data to the computing device when the second set of data is relevant to the first set of data.