Methods and apparatuses for monitoring energy consumption and related operations

• US 9,104,189 B2
• Filed: 06/29/2010
• Issued: 08/11/2015
• Est. Priority Date: 07/01/2009
• Status: Expired due to Fees

• Current Assignee: Carnegie Mellon University
• Original Assignee: Carnegie Mellon University
• Inventors: Berges Gonzalez, Mario E., Goldman, Ethan J., Soibelman, Luciio, Andrews, Burton W., Matthews, H. Scott, Hoynck, Michael, Benitez Meijia, Diego S.
• Primary Examiner(s): Raymond, Edward
• Application Number:
  US13/381,471
• Publication Number:
  US 20120290230A1
• Time in Patent Office: 1,869 Days
• Field of Search: 702/61, 702/64, 702182-185, 702/188
• US Class Current: 1/1
• CPC Class Codes: G01D 2204/24 Identification of individual loads, e.g. by analysing current/voltage waveforms
• CPC Class Codes: G01D 4/004 Remote reading of utility meters to a fixed location
• CPC Class Codes: G01R 19/2506 Arrangements for conditioning or analysing measured signals, e.g. for indicating peak values G01R19/003 takes precedence; Details concerning sampling, digitizing or waveform capturing displaying waveforms G01R13/00; analog sampling G01R19/0053
• CPC Class Codes: G05B 19/0428 Safety, monitoring G05B19/0423 takes precedence
• CPC Class Codes: G05B 2219/2639 Energy management, use maximum of cheap power, keep peak load low
• CPC Class Codes: G05B 2219/2642 Domotique, domestic, home control, automation, smart house
• CPC Class Codes: H02J 2310/14 The load or loads being home appliances
• CPC Class Codes: H02J 2310/70 Load identification
• CPC Class Codes: H02J 3/14 by switching loads on to, or off from, network, e.g. progressively balanced loading
• CPC Class Codes: Y02B 70/30 Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
• CPC Class Codes: Y02B 70/3225 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y02B 90/20 Smart grids as enabling technology in buildings sector smart grids supporting the management or operation of end-user stationary applications in general, or like technologies with no associated climate change mitigation effect Y04S20/00
• CPC Class Codes: Y04S 20/20 End-user application control systems
• CPC Class Codes: Y04S 20/222 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y04S 20/242 Home appliances
• CPC Class Codes: Y04S 20/30 Smart metering, e.g. specially adapted for remote reading

Abstract

Apparatus and method including acquiring a signal indicative of power consumption; detecting a transient in the signal; extracting a feature indicative of the transient, wherein extracting the feature includes: fitting a plurality of models to the transient, selecting one of the plurality of models as a model for the transient, and defining the feature indicative of the transient based on at least one parameter of the model for the transient; and classifying the transient based on the feature.

Claims

1. A method, comprising:

acquiring (50) a signal indicative of power consumption;

detecting (52) a transient in the signal;

extracting (54) a feature indicative of the transient, wherein extracting the feature includes;

fitting (60) a plurality of models to the transient;

selecting (62) one of the plurality of models as a model for the transient;

defining (64) the feature indicative of the transient based on at least one parameter of the model for the transient; and

classifying (56) the transient based on the feature.

2. The method of claim 1, further comprising after detecting a transient in the signal:

prompting (70) the user to identify the transient with an electrical device;

receiving (72) input from the user indicative of an identity of the transient;

classifying (74) the transient based on the input from the user.

3. The method of claim 1, wherein fitting (60) the plurality of models to the transient includes performing a regression of the transient.

4. The method of claim 1, wherein detecting (52) the transient includes:

defining (80) a window in the signal, wherein the window is less than all of the signal;

calculating (82) a likelihood ratio for each of a plurality of points within the window; and

identifying (84) a point within the window having a maximum likelihood ratio.

5. The method of claim 4, wherein calculating (82) the likelihood ratio includes:

calculating (90) a change in mean power;

setting (92) the likelihood ratio to zero if the change in mean power is less than a predetermined value.

6. The method of claim 4, further comprising after identifying (84) the point within the window having a maximum likelihood ratio:

moving (100) the window relative to the signal;

calculating (102) a likelihood ratio for each of a plurality of points within the window;

identifying (104) a point within the window having a maximum likelihood ratio.

7. The method of claim 6, wherein the steps of moving (100) the window relative to the signal, calculating (102) the likelihood ratio, and identifying (104) the point having a maximum likelihood ratio are performed a plurality of times.

8. The method of claim 7, further comprising identifying (120) a transient if at least one point satisfies a predetermined condition based on the likelihood ratio.

9. The method of claim 8, wherein identifying (120) the transient includes identifying the transient if at least one point is identified as having a maximum likelihood ratio in more than a predetermined number of windows.

10. The method of claim 4, wherein the likelihood ratio is a ratio of the probability that the point is drawn from a Gaussian distribution with mean and variance computed from a window of a predetermined number of samples before and the probability that the point was drawn from a Gaussian with mean and variance computed from a window of a predetermined number of samples after.

11. The method of claim 4, wherein calculating (82) a likelihood ratio includes calculating:

12. The method of claim 1, wherein:

acquiring (50) the signal includes acquiring a plurality of signals indicative of power consumption; and

the plurality of signals are selected from a group consisting of real power consumption, reactive power consumption, a harmonic of the real power consumption, a harmonic component of the reactive power consumption, current, and voltage.

13. A system (10), comprising:

a sensor (20);

an input device (16);

an output device (18);

a processor (12);

computer-readable memory (14) including computer-readable instructions which, when executed by the processor (12), cause the processor (12) to perform the steps of;

acquiring (50) a signal indicative of power consumption;

detecting (52) a transient in the signal;

extracting (54) a feature indicative of the transient, wherein extracting the feature includes;

fitting (60) a plurality of models to the transient;

selecting (62) one of the plurality of models as a model for the transient;

defining (64) the feature indicative of the transient based on at least one parameter of the model for the transient; and

classifying (56) the transient based on the feature.

14. The system (10) of claim 13, wherein the sensor (20) is a sensor of power consumption.

15. The system (10) of claim 13, further comprising an additional sensor (20) that does not sense power consumption.

16. The system (10) of claim 15, wherein the sensor (20) is selected from a group comprising a motion sensor, temperature sensor, acoustic sensor, light sensor, humidity sensor, oxygen level sensor, carbon dioxide sensor, radiation sensor, infrared sensor, pressure sensor, flow sensor electro-magnetic field sensor, and a particulates sensor.

17. The system (10) of claim 13, wherein the computer-readable instructions, when executed by the processor (12), cause the processor (12) to perform the following steps after detecting (52) the transient in the signal:

providing (70) a prompt on the output device (18), wherein the prompt requests a transient to be identified with an electrical device;

receiving (72) input at the input device (16); and

classifying (74) the transient based on the input.

18. The system (10) of claim 17, wherein providing (70) a prompt on the output device (18) includes identifying at least one electrical device that may correspond to the transient.

19. The system (10) of claim 17, wherein the providing (70) a prompt is performed immediately after the transient is detected.

20. The system (10) of claim 13, wherein the computer-readable instructions, when executed by the processor (12), cause the processor (12) to perform the following steps prior to detecting (52) the transient in the signal:

providing (70) a prompt on the output device (18), wherein the prompt requests electrical devices in the system to be identified;

receiving (72) input at the input device (16);

wherein the input includes at least one electrical device in the system; and

storing (58) data in computer-readable form, wherein the data is representative of the at least one electrical device in the system.

21. The system (10) of claim 17, wherein detecting (52) the transient in the signal includes detecting a plurality of transients in the signal, and further comprising after detecting a plurality of transients in the signal:

separating (130) the transients into a plurality of groups, wherein each group includes transients having a common feature;

providing (70) a prompt on the output device (18), wherein the prompt requests the plurality of the transients having a common feature to be identified with an electrical device;

receiving (72) input at the input device (16); and

classifying (74) the plurality of the transients having a common feature based on the input.

22. The system (10) of claim 21, wherein providing (70) a prompt on the output device (18) includes providing a list of electrical devices that may correspond with the plurality of transients.

23. The system of claim 17, wherein classifying (56) the transient based on the feature includes comparing the feature indicative of the transient with computer-readable data indicative of known signatures of electrical devices, wherein the computer-readable data indicative of known signatures of electrical devices are stored in computer-readable memory (14).