Methods and systems for analyzing energy usage

• US 8,386,086 B2
• Filed: 04/26/2010
• Issued: 02/26/2013
• Est. Priority Date: 04/26/2010
• Status: Active Grant

• Current Assignee: Accenture Global Services Limited (Accenture PLC)
• Original Assignee: Accenture Global Services Limited (Accenture PLC)
• Inventors: Souilmi, Younes, Kurth, Scott W., Akred, John M., Hoyem, Thorvald, Mathur, Sanjay, Roux, Galle Le
• Primary Examiner(s): KASENGE, CHARLES R
• Application Number:
  US12/767,259
• Publication Number:
  US 20110264291A1
• Time in Patent Office: 1,037 Days
• Field of Search: 700/286, 700/291, 700/295, 700/296, 702/57, 702/60, 702/61, 705/412
• US Class Current: 700/291
• CPC Class Codes: G01D 2204/14 Displaying of utility usage with respect to time, e.g. for monitoring evolution of usage or with respect to weather conditions
• CPC Class Codes: G01D 4/004 Remote reading of utility meters to a fixed location
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• 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: Y02P 90/82 Energy audits or management systems therefor
• CPC Class Codes: Y04S 20/30 Smart metering, e.g. specially adapted for remote reading

Abstract

Systems and methods consistent with the present invention allow an energy consumption index to be generated from DR response data and influencer data. The energy consumption index may indicate the energy consumption of a consumer before receiving a DR signal, the change in the consumption after the consumer receives a DR signal, and the consumer'"'"'s propensity to respond to a DR signal. Systems and methods consistent with the present invention also allow energy providers to monitor, forecast, and plan for changes in consumer demand for energy. Various energy planning tools may facilitate an energy provider'"'"'s ability to monitor, forecast, and plan for such changes.

Claims

1. A computer-implemented method of analyzing energy usage, comprising:

receiving demand response (“

DR”

) response data from a first group of energy users;

associating the DR response data with influencer data, stored in a database, regarding the first group of energy users;

determining, using a computer processor, an energy consumption index for the first group of energy users, wherein the energy consumption index comprises;

a value of energy consumption before DR data is communicated to devices corresponding to the first group of energy users;

a propensity of the first group of energy users to alter energy consumption in response to DR data; and

a change in energy consumption, after DR data is communicated to devices corresponding to the first group of energy users; and

establishing an energy consumption index for a second group of energy users based on the energy consumption index for the first group of energy users.

2. The method of claim 1, further comprising establishing an energy consumption index for a third group of energy users based on the energy consumption index for the second group of energy users.

3. The method of claim 1, wherein establishing the energy consumption index for the second group of energy users comprises correlating energy consumption behavior of the first group of energy users and energy consumption behavior of the second group of energy users.

4. The method of claim 1, wherein the energy consumption index for the first group of energy users further comprises a period over which energy change persisted.

5. The method of claim 1, wherein the second group of energy users is a subset of the first group of energy users.

6. The method of claim 1, wherein the DR response data comprise a value of energy consumption on a per-device basis.

7. The method of claim 1, wherein the influencer data consist of at least one of:

climatic conditions, location, customer attributes, and housing type for an energy consumer in the first group of energy users.

8. The method of claim 1, wherein the energy consumption index for the first group of energy users is displayed in a star schema.

9. The method of claim 8, wherein the energy consumption index for the first group of energy users is displayed in a three-dimensional cube format.

10. The method of claim 1, wherein the DR response data is communicated between devices corresponding to the first group of energy users and a third-party.

11. The method of claim 1, wherein the first group of energy users comprises a sample group of consumers that are representative of an energy provider'"'"'s customer base.

12. A system for managing energy usage by leveraging demand response (“

DR”

) data, comprising;

a first database storing DR response data received from usage devices corresponding a first group of energy users;

a second database storing influencer data regarding the first group of energy users;

a computer processor coupled to the first database and the second database, the computer processor associating the DR response data with the influencer data, the computer processor further determining an energy consumption index for the first group of energy users and establishing an energy consumption index for a second group of energy users based on the energy consumption index for the first group of energy users, wherein the energy consumption index for the first group of energy consumers comprises;

a value of energy consumption before DR data is communicated to usage devices corresponding to the first group of energy users;

a propensity of the first group of energy users to alter energy consumption in response to DR data; and

a change in energy consumption, after DR data is communicated to usage devices corresponding to the first group of energy users.

13. The system of claim 12, further comprising a computer server coupled to the first database programmed to receive DR response data from the usage devices corresponding to the first group of energy users.

14. The system of claim 12, wherein the first database and the second database comprise the same database file and are housed in the same hardware unit.

15. The system of claim 12, wherein the first group of energy users comprises a sample group of consumers that are representative of an energy provider'"'"'s customer base.

16. The system of claim 12, wherein the energy consumption index for the first group of energy users further comprises a period over which energy change persisted.

17. The system of claim 12, wherein the second group of energy users is a subset of the first group of energy users.

18. The system of claim 12, wherein establishing an energy consumption index for the second group of energy users comprises correlating energy consumption behavior of the first group of energy users and energy consumption behavior of the second group of energy users.

19. The system of claim 12, wherein the DR response data comprise a value of energy consumption on a per-device basis.

20. The system of claim 12, wherein the influencer data consists of at least one of:

climatic conditions, location, customer attributes, and housing type for an energy consumer in the first group of energy users.

21. The system of claim 12, wherein the energy consumption index for the first group of energy users is displayed in a star schema.

22. The system of claim 21, wherein the energy consumption index for the first group of energy users is displayed in a three-dimensional cube format.

23. The system of claim 12, wherein the DR response data is communicated between usage devices corresponding to the first group of energy users and a third-party.

24. A computer-implemented method of managing energy provision, comprising:

determining energy planning criteria for a group of energy users, wherein the energy planning criteria comprise;

a forecast, created using a computer, of a propensity of the group of energy users to alter energy consumption;

a forecast, created using a computer, of an energy consumption of the group of energy users; and

a determination of an actual energy consumption of the group of energy users;

determining an optimal level of a factor regarding energy provision based on the energy planning criteria;

determining a signal to send to receiving devices of a target group of energy users to achieve the optimal level of the factor regarding energy provision, wherein determining the signal to send comprises;

determining a type of demand response (“

DR”

) signal to send to the receiving devices of the target group of energy users;

determining the target group of energy users to whose receiving devices to send the DR signal; and

determining a timing scheme for sending the DR signal to the receiving devices of the target group of energy users;

sending the determined type of DR signal to the receiving devices of the determined target group of energy users in accordance with the determined timing scheme;

receiving response data from the determined target group of energy users'"'"' receiving devices; and

determining whether the energy consumption of the determined target group of energy users changed in response to the determined type of DR signal that was sent to the receiving devices of the determined target group of energy users.

25. The method of claim 24, further comprising simulating an effect of sending the determined type of DR signal to the receiving devices of the determined target group of energy users in accordance with the determined timing scheme.

26. The method of claim 24, wherein the factor regarding energy provision is the price of energy charged to the group of energy users.

27. The method of claim 24, wherein determining an optimal level of a factor regarding energy provision further comprises determining a price elasticity for the group of energy users.

28. The method of claim 24, wherein determining an optimal level of a factor regarding energy provision further comprises determining a demand elasticity for the group of energy users.

29. The method of claim 24, wherein determining an optimal level of a factor regarding energy provision further comprises simulating an effect of introducing a DR signal program into a new market.

30. The method of claim 24, wherein determining an optimal level of a factor regarding energy provision further comprises:

determining the existence of a problem in an energy provision network;

determining a type of DR signal to send to the receiving devices of the target group of energy users in order to alleviate the problem in the energy provision network.

31. The method of claim 30, wherein determining the type of DR signal to send further comprises determining a minimum level of energy load to be routed from a first area in the energy provision network to a second area in the energy provision network.

32. The method of claim 24, wherein determining an optimal level of a factor regarding energy provision further comprises determining a negawatt capacity of an energy provider.

33. The method of claim 32, wherein the negawatt capacity is graphically represented as a function of cost.