Systems and methods to predict a reduction of energy consumption

• US 8,972,071 B2
• Filed: 10/27/2011
• Issued: 03/03/2015
• Est. Priority Date: 10/27/2011
• Status: Expired due to Fees

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Meyerhofer, Mark Joseph, Schmid, James Joseph, Massey, Jerry Steven, Wilson, Bobby Antione, McCulley, Anthony Steven, Sierra, Jaime Alberto, Acharya, Sthitaprajna
• Primary Examiner(s): Shechtman, Sean
• Assistant Examiner(s): Rapp, Chad
• Application Number:
  US13/283,475
• Publication Number:
  US 20130110299A1
• Time in Patent Office: 1,223 Days
• Field of Search: 700/291, 700/295
• US Class Current: 700/291
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05F 1/66 Regulating electric power
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: Y02B 70/3225 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y04S 20/222 Demand response systems, e.g. load shedding, peak shaving

Abstract

A computing device for use with a demand response system is provided. The computing device includes a communication interface for receiving customer data of a plurality of customers, wherein the customer data includes a participation history and historical consumption values for each customer for participating in at least one demand response event. A processor is coupled to the communication interface and is programmed to select at least one customer from the plurality of customers by considering the participation history and the historical consumption values for each of the customers. The processor is also programmed to estimate a future reduction in energy consumption for the customer based on the customer data and to determine whether the estimated future reduction in energy consumption is substantially accurate.

Claims

1. A computing device for use with a demand response system, said computing device comprising:

a communication interface for receiving customer data of a plurality of customers, wherein the customer data includes a participation history and historical consumption values for each customer for participating in at least one demand response event;

a processor coupled to said communication interface and programmed to;

select at least one customer from the plurality of customers based at least in part on the participation history and the historical consumption values for each of the customers;

estimate a future reduction in energy consumption for the at least one selected customer based on the customer data; and

determine whether the estimated future reduction in energy consumption is substantially accurate.

2. A computing device in accordance with claim 1, wherein said processor is further programmed to calculate an average of an actual reduction of energy consumption by the at least one selected customer.

3. A computing device in accordance with claim 2, wherein said processor is programmed to determine whether the estimated future reduction in energy consumption is substantially accurate by comparing the estimated future reduction in energy consumption with the average of the actual reduction in energy consumption.

4. A computing device in accordance with claim 3, wherein said processor is further programmed to calculate a percentage of accuracy for the estimated future reduction in energy consumption.

5. A computing device in accordance with claim 1, wherein said communication interface receives customer data that includes an updated participation history and updated historical consumption values.

6. A computing device in accordance with claim 1, wherein said processor is programmed to select at least one customer by identifying at least one of the participation history that includes participating in at least three demand response events and the historical consumption values that correspond to the participation in the at least three demand response events.

7. A computing device in accordance with claim 1, wherein said processor is programmed to estimate the future reduction in energy consumption by considering at least one of a type of at least one demand response program that the at least one selected customer is enrolled in, the participation history for the at least one selected customer, a total numeric value of the customers participating in the at least one demand response event, and current and forecast weather conditions.

8. A demand response system comprising:

a data management system comprising a database that includes customer data of a plurality of customers, wherein the customer data includes a participation history and historical consumption values for each customer for participating in at least one demand response event; and

a computing device coupled to said data management system, said computing device comprising;

a communication interface for receiving the customer data;

a processor coupled to said communication interface and programmed to;

select at least one customer from the plurality of customers by considering the participation history and the historical consumption values for each of the customers;

estimate a future reduction in energy consumption for the at least one selected customer based on the customer data; and

determine whether the estimated future reduction in energy consumption is substantially accurate.

9. A demand response system in accordance with claim 8, wherein said processor is further programmed to calculate an average of an actual reduction of energy consumption by the at least one selected customer.

10. A demand response system in accordance with claim 9, wherein said processor is programmed to determine whether the estimated future reduction in energy consumption is substantially accurate by comparing the estimated future reduction in energy consumption with the average of the actual reduction in energy consumption.

11. A demand response system in accordance with claim 10, wherein said processor further programmed to calculate a percentage of accuracy for the estimated future reduction in energy consumption.

12. A demand response system in accordance with claim 8, wherein said communication interface receives customer data that includes an updated participation history and updated historical consumption values.

13. A demand response system in accordance with claim 8, wherein said processor is programmed to select at least one customer by identifying at least one of the participation history that includes participating in at least three demand response events and the historical consumption values that correspond to the participation in the at least three demand response events.

14. A demand response system in accordance with claim 8, wherein said processor is programmed to estimate the future reduction in energy consumption by considering at least one of a type of at least one demand response program that the at least one selected customer is enrolled in, the participation history for the at least one selected customer, a total numeric value of the customers participating in the at least one demand response event, and current and forecast weather conditions.

15. A method of predicting a reduction of energy consumption, said method comprising:

receiving, via a communication interface, customer data of a plurality of customers, wherein the customer data includes a participation history and historical consumption values for each customer for participating in at least one demand response event;

selecting, via a processor, at least one customer from the plurality of customers by considering the participation history and the historical consumption values for each of the customers;

estimating, via the processor, a future reduction in energy consumption for the at least one selected customer based on the customer data; and

determining, via the processor, whether the estimated future reduction in energy consumption is substantially accurate.

16. A method in accordance with claim 15, further comprising calculating, via the processor, an average of an actual reduction of energy consumption by the at least one selected customer.

17. A method in accordance with claim 15, wherein determining, via the processor, whether the estimated future reduction in energy consumption is substantially accurate further comprises determining, via the processor, whether the estimated future reduction in energy consumption is substantially accurate by comparing the estimated future reduction in energy consumption with an average of the actual reduction in energy consumption.

18. A method in accordance with claim 15, wherein determining, via the processor, whether the estimated future reduction in energy consumption is substantially accurate further comprises determining, via the processor, whether the estimated future reduction in energy consumption is substantially accurate by calculating a percentage of accuracy for the estimated future reduction in energy consumption.

19. A method in accordance with claim 15, further comprising updating the participation history and the historical consumption values for each customer.

20. A method in accordance with claim 15, wherein selecting, via a processor, at least one customer of the plurality of customers further comprises selecting, via a processor, at least one customer of the plurality of customers by identifying at least one of the participation history that includes participating in at least three demand response events and the historical consumption values that correspond to the participation in the at least three demand response events.