Offered actions for energy management based on anomalous conditions

• US 8,473,107 B2
• Filed: 08/05/2010
• Issued: 06/25/2013
• Est. Priority Date: 08/05/2010
• Status: Active Grant

• Current Assignee: NantEnergy, Inc.
• Original Assignee: Sharp Laboratories of America Incorporated (Hon Hai Precision Industry Co., Ltd.)
• Inventors: Park, Daniel J.
• Primary Examiner(s): Bahta, Kidest
• Application Number:
  US12/850,833
• Publication Number:
  US 20120035777A1
• Time in Patent Office: 1,055 Days
• Field of Search: 700/276, 700/286, 700/291, 340/12.37, 340/573.4, 701/18, 379/38, 705/412
• US Class Current: 700/276
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: H02J 13/00002 characterised by monitoring
• CPC Class Codes: H02J 13/00004 characterised by the power network being locally controlled
• CPC Class Codes: H02J 13/00028 involving the use of Internet protocols
• 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: Y02E 60/00 Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
• CPC Class Codes: Y04S 10/30 State monitoring, e.g. fault, temperature monitoring, insulator monitoring, corona discharge
• CPC Class Codes: Y04S 20/221 General power management systems
• CPC Class Codes: Y04S 20/222 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y04S 20/242 Home appliances

Abstract

Systems and devices for, and methods of, energy management via prompted response options based on detected anomalous conditions.

Claims

1. A method of energy management at a site comprising:

associating, by a computing device, based on a list of potential situations extracted from a data store, a detected anomalous energy state occurrence with a set of one or more contextual attributes of the site;

determining, by the computing device, a set of proposed responses per each member of a set of proposed causal situations, the determination based on a likelihood that a causal situation occurred in similar sites and a likelihood that a causal situation occurred for the site based on confirmed historical occurrences;

wherein the set of proposed responses is a countermeasure to the causal situation associated with a corrective action and the set of proposed responses is determined to be effective after a timeout function is invoked; and

providing, based on the set of one or more contextual attributes, the set of proposed responses per each member of a set of proposed causal situations.

2. The method of claim 1 further comprising:

revising the set of proposed causal situations based on an input designation of a proposed response.

3. The method of claim 1 wherein in the step of providing, based on the set of one or more contextual attributes, the set of proposed responses per each member of the set of proposed causal situations is provided via a user interface for input designation.

4. The method of claim 1 further comprising:

revising the set of proposed responses of at least one member of the set of proposed causal situations based on an input designation of a proposed response.

5. The method of claim 1 wherein the set of proposed causal situations is provided in a ranked order based on likelihood estimates per each member of the set of proposed causal situations.

6. The method of claim 1 further comprising applying a weighting factor to at least one member of the set of proposed causal situations, and wherein the set of proposed causal situations is provided in a ranked order based on weighted likelihood estimates.

7. The method of claim 6 wherein the weighting factor is based on a frequency of input designation of a proposed response.

8. The method of claim 1 further comprising applying a weighting factor to at least one member of the set of proposed responses, and wherein the set of proposed responses is provided in a ranked order based on numerical weight.

9. The method of claim 8 wherein the weighting factor is based on a frequency of input designation of a proposed response.

10. The method of claim 1 wherein a detected anomalous energy consumption occurrence is based on a comparison of estimated energy consumption and expected energy consumption based on the set of one or more contextual attributes of the site.

11. The method of claim 10 wherein the set of one or more contextual attributes are derived from a simulated diurnal-seasonal model of the site.

23. The method of claim 1 wherein the determining of the set of proposed responses is based on at least one of:

a set of user selections, general knowledge based on preloaded heuristics, and information obtained from a plurality of other sites.

24. The method of claim 6 wherein the weighting factor comprises a set of one or more attributes, wherein the set of one or more attributes are determined based on at least one of:

(a) an effectiveness of restoring the normal state of the site;

(b) a monetary cost or an environmental cost of a proposed response;

(c) a likelihood of a user performing the proposed response; and

(d) a previous experience of the user taking or ignoring the proposed response.

12. A device for energy management at a site, the device comprising:

a processor, and addressable memory, the addressable memory comprising a set of one or more contextual attributes of the site, wherein the processor is configured to;

associate a detected anomalous energy state occurrence with the set of one or more contextual attributes of the site, based on a list of potential situations extracted from a data store;

determine a set of proposed responses per each member of a set of proposed causal situations, the determination based on a likelihood that a causal situation occurred in similar sites and a likelihood that a causal situation occurred for the site based on confirmed historical occurrences;

wherein the set of proposed responses is a countermeasure to the causal situation associated with a corrective action and the set of proposed responses is determined to be effective after a timeout function is invoked; and

provide, based on the set of one or more contextual attributes, the set of proposed responses per each member of a set of proposed causal situations.

13. The device of claim 12 wherein the processor is further configured to revise the set of proposed causal situations based on an input designation of a proposed response.

14. The device of claim 12 wherein the processor is further configured to provide, based on the set of one or more contextual attributes, the set of proposed responses per each member of the set of proposed causal situations, via a user interface for input designation.

15. The device of claim 12 wherein the processor is further configured to revise the set of proposed responses of at least one member of the set of proposed causal situations based on an input designation of a proposed response.

16. The device of claim 12 wherein the processor is further configured to provide the set of proposed causal situations in a ranked order based on likelihood estimates per each member of the set of proposed causal situations.

17. The device of claim 12 wherein the processor is further configured to apply a weighting factor to at least one member of the set of proposed causal situations, and wherein the processor is further configured to provide the set of proposed causal situations in a ranked order based on weighted likelihood estimates.

18. The device of claim 17 wherein the weighting factor is based on a frequency of input designation of a proposed response.

19. The device of claim 18 wherein the processor is further configured to apply a weighting factor to at least one member of the set of proposed responses, and wherein the processor is further configured to provide a set of proposed responses in a ranked order based on numerical weight.

20. The device of claim 19 wherein the weighting factor is based on a frequency of input designation of a proposed response.

21. The device of claim 12 wherein a detected anomalous energy consumption occurrence is based on a comparison of estimated energy consumption and expected energy consumption based on the set of one or more contextual attributes of the site.

22. The device of claim 21 wherein the processor is further configured to derive the set of one or more contextual attributes from a simulated diurnal-seasonal model of the site.