Systems and methods to predict a reduction of energy consumption

• US 9,262,718 B2
• Filed: 02/13/2015
• Issued: 02/16/2016
• Est. Priority Date: 10/27/2011
• Status: Active Grant

• Current Assignee: GE Digital Holdings LLC (GE Aerospace)
• 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): Lo, Kenneth M
• Assistant Examiner(s): Rapp, Chad
• Application Number:
  US14/622,389
• Publication Number:
  US 20150154499A1
• Time in Patent Office: 368 Days
• Field of Search: 700/291, 700/295
• US Class Current: 1/1
• 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 demand response monitoring (DRM) computing device for use with a demand response system, said DRM computing device comprising:

a communication interface configured to receive customer data for a plurality of customers, wherein the customer data includes a participation history for each customer that participated in at least one demand response event; and

a processor coupled to a memory device and to said communication interface, said processor programmed to;

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

estimate a future reduction in energy consumption for the at least one selected customer based on a total numeric value of customers that participate in a particular demand response event, typical weather conditions, and types of demand response events that the at least one selected customer participates in during different weather conditions.

2. A DRM computing device in accordance with claim 1, wherein said processor is further programmed to:

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

calculate an average of an actual reduction of energy consumption by the at least one selected customer.

3. A DRM 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 DRM 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 DRM computing device in accordance with claim 1, wherein said communication interface receives customer data that includes an updated participation history.

6. A DRM 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 historical consumption values that correspond to the participation in the at least three demand response events.

7. A DRM 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 that participated 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 for a plurality of customers, wherein the customer data includes a participation history for each customer that participated in at least one demand response event; and

a demand response monitoring (DRM) computing device coupled to said data management system, said DRM computing device comprising;

a communication interface configured to receive the customer data;

a processor coupled a memory and to said communication interface, said processor programmed to;

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

estimate a future reduction in energy consumption for the at least one selected customer based on a total numeric value of customers that participate in a particular demand response event, typical weather conditions, and types of demand response events that the at least one selected customer participates in during different weather conditions.

9. A demand response system in accordance with claim 8, wherein said processor is further programmed to:

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

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.

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 that participated in the at least one demand response event, and current and forecast weather conditions.

15. A computer-implemented method for predicting a reduction of energy consumption, said method implemented using a processor in communication with a memory, said method comprising:

receiving customer data for a plurality of customers, wherein the customer data includes a participation history for each customer for that participated in at least one demand response event;

selecting at least one customer from the plurality of customers by considering the participation history for each of the customers; and

estimating a future reduction in energy consumption for the at least one selected customer based on a total numeric value of customers that participate in a particular demand response event, typical weather conditions, and types of demand response events that the at least one selected customer participates in during different weather conditions.

16. A method in accordance with claim 15, further comprising:

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

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 16, 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 the average of the actual reduction in energy consumption.

18. A method in accordance with claim 17, 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, 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.