Non-Intrusive Load Disaggregation via Factor Graph Analysis
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Solution Overview
Problem
Current methods for electrical load disaggregation in composite electrical environments are invasive, costly, and inefficient, as they require multiple sensors and devices to monitor individual appliance energy consumption, making large-scale deployment cumbersome and expensive.
Innovation Solution
A system utilizing a processor and memory with input, factor-graph, and rule engine modules to perform non-intrusive load disaggregation by analyzing active/reactive power levels, power factors, and contextual information, generating confidence measures and optimizing load identification through factor-graph analysis and rule-based decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple recording devices or sensors are used to monitor individual appliance energy consumption, then measurement precision is improved, but device complexity and installation cost increase
Solution Approach 1:
The patent segments the composite load signal into individual appliance signatures through signal processing techniques. Instead of using multiple physical sensors, the system divides the composite electrical signal into constituent parts representing different appliances, achieving precise measurement without increasing device complexity
Solution Approach 2:
The patent introduces an intermediary signal processing layer that acts as a mediator between the composite load measurement and individual appliance identification. Through waveform analysis and pattern recognition, this intermediary process extracts individual appliance signatures from the composite signal, resolving the contradiction between measurement precision and system complexity
2Measurement precision
If multiple recording devices or sensors are inserted between sockets and appliances, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent extracts individual appliance information from the composite load signal without physically inserting devices at appliance outlets. By taking out and isolating specific appliance signatures through signal analysis, the system achieves precise monitoring while maintaining ease of deployment at a single central location
Solution Approach 2:
The patent creates virtual copies of individual appliance measurements from a single composite signal measurement. Through signal processing, the system generates separate monitoring data for each appliance as if individual sensors were present, achieving the same monitoring capability without the physical complexity of multiple devices
3Measurement precision
If intrusive monitoring devices are deployed at consumer locations, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent makes the single smart meter universal by enabling it to perform both aggregate energy measurement and individual appliance monitoring functions. Through advanced signal processing algorithms, the smart meter achieves multi-functionality, simultaneously providing overall load measurement and detailed appliance-level disaggregation without requiring separate specialized devices
Data Source
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AI summary
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.