System and method for remote identification of energy consumption systems and components

• US 20050228599A1
• Filed: 06/10/2005
• Published: 10/13/2005
• Est. Priority Date: 12/04/2001
• Status: Active Grant

• Current Assignee: Texas A&M University System
• Original Assignee: Texas A&M University System
• Inventors: Turner, William D., Haberl, Jeffrey S., Culp, Charles H., Claridge, David E.
• Granted Patent: US 7,225,089 B2
• Time in Patent Office: Days
• US Class Current: 702/62
• CPC Class Codes: G01D 4/004 Remote reading of utility meters to a fixed location
• CPC Class Codes: G01R 21/06 by measuring current and voltage G01R21/08 - G01R21/133 take precedence
• CPC Class Codes: G06Q 50/06 Energy or water supply
• 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 20/30 Smart metering, e.g. specially adapted for remote reading

Abstract

A system for remote energy consumption system identification of a facility includes a processor, and a memory coupled to the processor. The system also includes an energy consumption database accessible by the processor. The energy consumption database includes aggregated energy consumption data associated with the facility. The system also includes a facility database accessible by the processor. The facility database includes facility data associated with the facility. The system further includes an external variable database accessible by the processor. The external variable database includes external variable data corresponding to the energy consumption data. The system also includes an analysis engine residing in the memory and executable by the processor. The analysis engine is operable to generate disaggregated energy consumption data using the aggregated energy consumption data, the facility data, and the external variable data, and identify an energy consumption system of the facility using the disaggregated energy consumption data.

Claims

1. A method for remote energy consumption system identification of a facility, comprising:

receiving aggregated energy consumption data associated with the facility;

receiving external variable data for the facility corresponding to the aggregated energy consumption data;

generating facility data associated with the facility from the external variable data and the aggregated energy consumption data;

generating disaggregated energy consumption data for the facility from the aggregated energy consumption data using the facility data and the external variable data; and

identifying an energy consumption system of the facility using the disaggregated energy consumption data.

2. The method of claim 1, further comprising validating the aggregated energy consumption data.

3. The method of claim 2, wherein validating the aggregated energy consumption data comprises:

analyzing the aggregated energy consumption data for missing data; and

reconstructing the missing data.

4. The method of claim 3, wherein reconstructing the missing data comprises:

identifying a comparable facility;

retrieving energy consumption data associated with the comparable facility; and

reconstructing the missing data using the comparable facility energy consumption data.

5. The method of claim 1, wherein receiving the aggregated energy consumption data comprises receiving the aggregated energy consumption data from an energy consumption database of an energy supplier.

6. The method of claim 1, wherein receiving the aggregated energy consumption data comprises receiving the aggregated energy consumption data from a data collector disposed at the facility.

7. The method of claim 1, wherein identifying an energy consumption system comprises identifying an energy consumption system for controlling an internal environment of the facility.

9. The method of claim 1, wherein generating the facility data further comprises generating the facility data based on physical characteristics of the facility.

10. The method of claim 1, wherein receiving the external variable data comprises receiving environmental data corresponding to the aggregated energy consumption data.

11. The method of claim 10, wherein receiving the environmental data comprises receiving the environmental data from an environmental service.

12. The method of claim 1, further comprising determining a modification of operating parameters of the energy consumption system using the disaggregated energy consumption data.

13. The method of claim 1, wherein generating disaggregated energy consumption data comprises determining energy consumption for a particular time interval from the aggregated energy consumption data, and wherein identifying an energy consumption system comprises identifying the energy consumption system from the energy consumption for the particular time interval.

14. The method of claim 1, wherein generating disaggregated energy consumption data comprises:

accessing an energy consumption database of an energy supplier, the energy consumption database having energy consumption data associated with facility; and

evaluating the energy consumption data of the energy supplier energy consumption database and the facility data to generate the disaggregated energy consumption data corresponding to the facility.

8. (canceled)

15. A system for remote energy consumption system identification of a facility, comprising:

a processor;

a memory coupled to the processor;

an energy consumption database accessible by the processor, the energy consumption database having aggregated energy consumption data associated with the facility;

an external variable database accessible by the processor, the external variable database having external variable data corresponding to the energy consumption data;

a facility-independent facility database having facility data generated using the aggregated energy consumption data;

the facility-independent facility database and the external variable database both accessible by the processor; and

an analysis engine residing in the memory and executable by the processor, the analysis engine operable to generate disaggregated energy consumption data using the aggregated energy consumption data, the facility data, and the external variable data, the analysis engine further operable to identify an energy consumption system of the facility using the disaggregated energy consumption data.

16. The system of claim 15, further comprising a validation engine residing in the memory and executable by the processor, the validation engine operable to validate the aggregated energy consumption data.

17. The system of claim 16, wherein the validation engine is operable to analyze the aggregated energy consumption data for missing data and, in response to determining that missing data exists, reconstruct the missing data.

18. The system of claim 17, wherein the processor is further operable to access energy consumption data associated with a comparable facility, and wherein the validation engine is operable to reconstruct the missing data using the comparable facility energy consumption data.

19. The system of claim 15, wherein the aggregated energy consumption data comprises aggregated energy consumption data residing in an energy consumption database of an energy supplier.

21. The system of claim 15, further comprising a data collector disposed at the facility, the data collector operable to transmit the aggregated energy consumption data to the processor.

23. The system of claim 15, wherein the facility data comprises facility data based on physical characteristics of the facility.

24. The system of claim 15, wherein the external variable data comprises environmental data corresponding to the aggregated energy consumption data.

25. The system of claim 15, wherein the analysis engine is further operable to determine operating parameters of the energy consumption system using the disaggregated energy consumption data.

26. The system of claim 25, wherein the analysis engine is further operable to determine a modification to the operating parameters of the energy consumption system from the disaggregated energy consumption data.

22. (canceled)

27. A method for remote energy consumption system identification of a facility, comprising:

receiving aggregated energy consumption data associated with the facility;

receiving external variable data for the facility corresponding to the aggregated energy consumption data;

generating facility data associated with the facility from the external variable data and the aggregated energy consumption data;

generating disaggregated energy consumption data for the facility from the aggregated energy consumption data using the external variable data and the generated facility data supplemented by received facility data; and

identifying an energy consumption system of the facility using the disaggregated energy consumption data.

28. The method of claim 27, further comprising validating the aggregated energy consumption data by:

analyzing the aggregated energy consumption data for missing data; and

reconstructing the missing data.

29. A system for remote energy consumption system identification of a facility, comprising:

a processor;

a memory coupled to the processor;

an energy consumption database accessible by the processor, the energy consumption database having aggregated energy consumption data associated with the facility;

an external variable database accessible by the processor, the external variable database having external variable data corresponding to the energy consumption data;

a facility-independent facility database generated using the aggregated energy consumption data and the external variable database;

the facility-independent facility database and the external variable database both accessible by the processor; and

an analysis engine residing in the memory and executable by the processor, the analysis engine operable to generate disaggregated energy consumption data using the aggregated energy consumption data, the facility data, and the external variable data, the analysis engine further operable to remotely identify an energy consumption component of the facility using the disaggregated energy consumption data.