Abstract: Disclosed is a method and system for optimizing a composite load disaggregation. The system comprises an input module, a factor graph module, a contextual information database, a rule engine, a priori database and a rule database. The factor-graph module is configured to perform factor-graph analysis on one or more input variables received from the input module to generate confidence measures wherein the confidence measures indicate the composite load disaggregation. The method and system is enabled to retrieve contextual information from the contextual information database. The method and system is further enabled to optimize the composite load disaggregation by means of the rule engine. The rule engine is adapted to retrieve one or more rules from the rule database and further adapted to apply retrieved rules to the confidence measures and to the contextual information for identifying at least one appliance from one or more appliances in an electrical environment.

Abstract: Disclosed is a method and system for optimizing a composite load disaggregation. The system comprises an input module, a factor graph module, a contextual information database, a rule engine, a priori database and a rule database. The factor-graph module is configured to perform factor-graph analysis on one or more input variables received from the input module to generate confidence measures wherein the confidence measures indicate the composite load disaggregation. The method and system is enabled to retrieve contextual information from the contextual information database. The method and system is further enabled to optimize the composite load disaggregation by means of the rule engine. The rule engine is adapted to retrieve one or more rules from the rule database and further adapted to apply retrieved rules to the confidence measures and to the contextual information for identifying at least one appliance from one or more appliances in an electrical environment.