NON-INTRUSIVE APPLIANCE LOAD IDENTIFICATION USING CASCADED COGNITIVE LEARNING

• US 20100305889A1
• Filed: 05/27/2009
• Published: 12/02/2010
• Est. Priority Date: 05/27/2009
• Status: Abandoned Application

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Khalifa, Yaser, Tiwari, Rashi, Tomlinson, Harold Woodruff JR., Durling, Michael Richard
• Application Number:
  US12/472,650
• Publication Number:
  US 20100305889A1
• Time in Patent Office: Days
• US Class Current: 702/62
• CPC Class Codes: G01D 1/00 Measuring arrangements giving results other than momentary value of variable, of general application G01D3/00 takes precedence; in tariff metering apparatus G01D4/00; transducers not specially adapted for a specific variable G01D5/00
• CPC Class Codes: G01D 15/00 Component parts of recorders for measuring arrangements not specially adapted for a specific variable
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

A method of identifying energy consumption associated with at least one appliance is provided. The method includes measuring an energy consumption signal, obtaining publicly available information of a location of the at least one appliance and estimating a plurality of probabilities of energized appliances based on the energy consumption signal and the publicly available information. The method further includes generating a new combination of the estimated plurality of probabilities of energized appliances and decomposing the at least one energy consumption signal into constituent individual loads and corresponding energy consumption.

Claims

1. An energy measurement system comprising:

at least one sensor configured to measure at least one output signal associated with a plurality of appliances;

an orientation module configured to gather publicly available information associated with a location of the appliances;

a planning module configured to generate an appliance database based on an input signal from the orientation module;

a decomposition module configured to decompose the at least one output signal into constituent individual loads and therefrom identify energy consumption corresponding to each appliance based on the appliance database;

a communication interface configured to transmit the decomposed output signal.

2. The system of claim 1 wherein the at least one sensor, the orientation, planning, decomposition modules, and the communication interface are housed within an energy meter.

3. The system of claim 1 wherein the at least one sensor is housed within an energy meter, and wherein the orientation, planning, and decomposition modules, and the communication interface situated at a remote location.

4. The system of claim 1, further comprising at least one sensor configured to measure environmental data, and wherein the decomposition module is configured to use the environmental data.

5. The system of claim 1, wherein the at least one output signal is selected from a current signal, a voltage signal, an admittance signal, an impedance signal, a total power signal and combinations thereof.

6. The system of claim 5, wherein the decomposition module is further configured to decompose the at least one output signal based on a power difference between the total power signal and an estimated total power from the appliance database.

7. The system of claim 1, wherein the publicly available information comprises an Internet database.

8. The system of claim 7, wherein the internet database comprises an aerial imagery of a house from a Google Maps™

mapping service or a house details from Zillow.com®

real estate service.

9. The system of claim 1, wherein the state matrix comprises a state of the appliance and estimated, known, or measured information about the appliance.

10. The system of claim 1, wherein the orientation module is further configured to gather information from a local information database and an appliance template database.

11. The system of claim 6, wherein the decomposition module comprises:

an appliance probability estimator configured to estimate a plurality of probabilities of energized appliances; and

an appliance combination estimator configured to generate a new combination of the plurality of probabilities of energized appliances based on the power difference.

12. The system of claim 11, wherein the appliance probability estimator comprises a Markov Chain algorithm or a hidden Markov Chain algorithm.

13. The system of claim 11, wherein the appliance probability estimator comprises a Bayesian algorithm comprising at least one classifier.

14. The system of claim 13, wherein the at least one classifier comprises a temperature classifier, a time classifier, a power classifier, a voltage classifier, a load type classifier, a geographic location classifier or any combinations thereof.

15. The system of claim 11, wherein the appliance probability estimator is further configured to generate an estimated total power based on the sum of estimated energy consumption of the individual loads.

16. The system of claim 11, wherein the appliance combinatorial estimator comprises a genetic algorithm.

17. A method for identifying energy consumption associated with at least one appliance comprising:

measuring an energy consumption signal;

obtaining publicly available information of a location of the at least one appliance;

estimating a plurality of probabilities of energized appliances based on the energy consumption signal and the publicly available information;

generating a new combination of the estimated plurality of probabilities of energized appliances; and

decomposing the at least one energy consumption signal into constituent individual loads and corresponding energy consumption.

18. The method of claim 17, wherein the energy consumption signal is selected from a current signal, a voltage signal, an admittance signal, an impedance signal, a total power signal and combinations thereof.

19. The method of claim 17, wherein the publicly available information comprises an Internet database.

20. The method of claim 17, further comprising generating an estimated total power based on the sum of estimated energy consumption of the individual loads;

obtaining a difference between the energy consumption signal and the estimated total power; and

using the difference to determine whether further generation of the new combination of the estimated plurality of probabilities is required.

21. The method of claim 17, wherein estimating the plurality of probabilities comprises filtering the estimated probabilities based on a classification comprising a temperature classification, a time classification, a power classification, a voltage classification, a load type classification, a geographic location classification or any combinations thereof.

22. The method of claim 17, wherein generating the new combination comprises determining a schema based on the estimated probabilities.

23. The method of claim 17, wherein generating the new combination comprises using a genetic algorithm method.

24. The method of claim 23, wherein the genetic algorithm method comprises the steps of selection, crossover and mutation.

25. An energy measurement system comprising:

at least one sensor configured to measure at least one output signal associated with a plurality of appliances;

a communication interface configured to transmit the at least one output signal to a remote utility station;

an orientation module configured to gather publicly available information associated with a location of the appliances;

a planning module configured to generate an appliance database based on an input signal from the orientation module;

a decomposition module configured to decompose the at least one output signal into constituent individual loads and therefrom identify energy consumption corresponding to each appliance based on the appliance database;

wherein the orientation module, the planning module and the decomposition module are located at the remote utility station.