General Electric Company

Chad Eric Knodle

James Joseph Schmid

John Wesley Grant

Alan Thomas Schachtely

Richard Lewis Gomer

Bradley Ted Kelly

Patrick Louis Pfrehm

Piero Patrone Bonissone

Weizhong Yan

Naresh Sundaram Iyer

Rajesh Venkat Subbu

Rasiklal Punjalal Shah

Jerry Steven Massey

Mark Joseph Meyerhofer

Bobby Antione Wilson

Neil H Eklund

Ninad Vijaykumar Kulkarni

Certain embodiments of the invention may include systems, methods, and apparatus for estimating demand response load change. According to an example embodiment of the invention, a method is provided for estimating energy usage change associated with a demand event. The method may include determining energy potential change during a demand event for one or more devices associated with a location; estimating energy usage patterns for the location over predefined time periods before and after the demand event; determining pre-demand event and post-demand event energy rebounds based at least in part on the estimated energy usage patterns; determining an energy change profile associated with the location based at least in part on the determined energy potential change, and the pre-demand event and post-demand event energy rebounds.

Demand response load reduction estimation

A method of managing a machinery monitoring system is provided. The machinery monitoring system includes a database of at least one rule set, the rule set including at least one rule expressed as a relational expression of a real-time data output relative to a real-time data input, the relational expression being specific to a plant asset. The method includes importing data representative of a rule set into the machinery monitoring system, applying the at least one rule set to a specific plant asset wherein the at least one rule set is configured to locate the data input using at least a portion of the relative path information, determining a data output of the at least one rule set using the at least one relational expression and the data input, and transmitting the data output to at least one of the machinery monitoring system and the plant monitoring and control system.

Methods and systems for monitoring and diagnosing machinery

A computer-implemented method of managing a machinery monitoring system is provided. The method includes relating an asset output to at least one asset input, generating at least one rule based on the relation, selecting at least one of live asset data, historical asset data, user-supplied asset data, and third party supplied asset data to test the at least one rule, testing the at least one rule incrementally using the selected asset data, and monitoring the output of the at least one rule at each increment.

Methods and systems for monitoring and diagnosing machinery by incremently testing a rule

A method and system for performing model-based multi-objective asset optimization and decision-making is provided. The method includes building at least two predictive models for an asset. The building includes categorizing operational historical data via at least one of: controllable variables, uncontrollable variables, output objectives, and constraints. The building also includes selecting at least two output objectives or constraints, and identifying at least one controllable or uncontrollable variable suitable for achieving the at least two output objectives or constraints. The method also includes validating each predictive model and performing multi-objective optimization using the predictive models. The multi-objective optimization includes specifying search constraints and applying a multi-objective optimization algorithm. The method further includes generating a Pareto Frontier, and selecting a Pareto optimal input-output vector.

Method and system for performing model-based multi-objective asset optimization and decision-making

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Dayton, NV, United States of America

Chad Eric Knodle