USERS CAMPAIGN FOR PEAKING ENERGY USAGE

• US 20160042049A1
• Filed: 12/23/2014
• Published: 02/11/2016
• Est. Priority Date: 08/07/2014
• Status: Active Grant

• Current Assignee: Opower, Inc. (Oracle Corporation)
• Original Assignee: Opower, Inc. (Oracle Corporation)
• Inventors: Yu, Suelyn, Royer, David, Shilts, Erik, Chung, Garner
• Granted Patent: US 10,467,249 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
• 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: G06Q 50/06 Energy or water supply
• CPC Class Codes: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: Y02D 10/00 Energy efficient computing, e.g. low power processors, power management or thermal management

Abstract

Aspects of the subject technology relate to a system that analyzes customers'"'"' AMI load curves, identifies evening peak users as defined by their load curves, and provides Energy Efficiency (EE) advice related to their periods of high use. For example, identified high evening users can be sent an email with normative comparisons on evening load use, along with tips to reduce energy usage. Other aspects relate to additional targets/communications. Aspects of the subject technology relate to categorizing a user'"'"'s energy consumption tendencies based on a user'"'"'s load curve and providing customized content based on the user'"'"'s category. By taking into consideration the user'"'"'s actual energy consumption patterns, the system may be able to provide more relevant content to the user.

Claims

1. A computer-implemented method for implementing an evening peak user campaign, comprising:

aggregating consumption data for a plurality of users, the consumption data corresponding with an amount of energy resource consumption for each of the respective plurality of users;

generating, from the consumption data, at least one load curve for the plurality of users;

identifying, from the at least one load curve, a subset of the plurality of users that are peak users that consume more resources during a first time period than a second time period;

generating a plurality of use reports for the peak users, wherein each of the use reports provides a visual display of the consumption data for each of the peak users and energy efficiency advice related to consumption during the first time period; and

providing one or more of the use reports to one or more of the peak users to reduce the amount of energy resource consumption for the one or more peak users.

2. The computer-implemented method of claim 1, wherein the peak users are users that consume more resources during evening hours than other hours in a day.

3. The computer-implemented method of claim 1, where at least one curve from the load curves includes a peak portion that indicates a higher usage of resources during a portion of a time period in comparison with remaining portions of the time period.

4. The computer-implemented method of claim 1, wherein generating load curves for the plurality of users further comprises:

determining one or more values measuring energy consumption at specified intervals over a specified time period, each of the one or more values at the specified intervals representing an average amount of energy usage at a specified interval of the specified time period;

determining a total amount of energy usage for the specified time period;

for each value measured at each specified interval over the specified time period, determining a percentage of the total amount of energy usage based on the value; and

generating a graphical representation of a respective load curve based at least in part on the percentage of the total amount of energy usage at each of the specified intervals over the specified time period.

5. The computer-implemented method of claim 3, wherein the specified time period is a day and each of the specified intervals are respective hours in the day.

6. The computer-implemented method of claim 3, wherein the values measuring energy consumption at specified intervals over the specified time period further represent a standard deviation of usage at a respective interval, or a baseload at the respective interval.

7. The computer-implemented method of claim 1, wherein identifying the subset of the plurality of users that are peak users further comprises:

determining one or more load curve archetypes, each load curve archetype including a respective load curve that represents a type of user based on energy consumption tracked during a period of time; and

using a clustering algorithm to segment users into one or more categories based on the load curves and the one or more load curve archetypes.

8. The computer-implemented method of claim 6, wherein the clustering algorithm comprises a k-means algorithm.

9. The computer-implemented method of claim 6, wherein the one or more categories comprise at least one of an evening peak user, a morning peak user, a mid-day peak user, or a peak user corresponding to a season within a year.

10. The computer-implemented method of claim 1, wherein each of the plurality of use reports for the peak users includes a graphical representation of a respective load curve corresponding to a respective peak user.

11. A computing device for implementing an evening peak user campaign, the computing device comprising:

at least one processor; and

memory storing instructions that, when executed by the at least one processor, cause the computing device to;

aggregate consumption data for a plurality of users, the consumption data corresponding with an amount of energy resource consumption for each of the respective plurality of users;

generate, from the consumption data, load curves for the plurality of users; and

identify, based at least in part on the load curves, a new user that is indicated as a peak user, the new user being a different user other than the plurality of users, the new user consuming more resources during a first time period than a second time period.

12. The computing device of claim 11, wherein the instructions further cause the at least one processor to:

generate a use report for the peak user, wherein the use report provides a visual display of the consumption data for the peak user and energy efficiency advice related to consumption during the first time period; and

provide the use report to the peak user to reduce the amount of energy resource consumption for the peak user.

13. The computing device of claim 11, wherein the peak user consumes more resources during evening hours than other hours in a day.

14. The computing device of claim 11, where at least one curve from the load curves includes a peak portion that indicates a higher usage of resources during a portion of a time period in comparison with remaining portions of the time period.

15. The computing device of claim 11, wherein to generate load curves for the plurality of users further comprises:

determining one or more values measuring energy consumption at specified intervals over a specified time period, each of the one or more values at the specified intervals representing an average amount of energy usage at a specified interval of the specified time period;

determining a total amount of energy usage for the specified time period;

for each value measured at each specified interval over the specified time period, determining a percentage of the total amount of energy usage based on the value; and

generating a graphical representation of a respective load curve based at least in part on the percentage of the total amount of energy usage at each of the specified intervals over the specified time period.

16. The computing device of claim 15, wherein the specified time period is a day and each of the specified intervals are respective hours in the day.

17. The computing device of claim 15, wherein the values measuring energy consumption at specified intervals over the specified time period further represent a standard deviation of usage at a respective interval, or a baseload at the respective interval.

18. The computing device of claim 11, wherein identifying the peak user further comprises:

determining one or more load curve archetypes, each load curve archetype including a respective load curve that represents a type of user based on energy consumption tracked during a period of time; and

using a clustering algorithm to identify a respective user as the peak user based on a load curve of the peak user and one or more categories represented by the one or more load curve archetypes.

19. The computing device of claim 18, wherein the one or more categories comprise at least one of an evening peak user, a morning peak user, a mid-day peak user, or a peak user corresponding to a season within a year.

20. A non-transitory computer readable storage medium storing instructions for implementing an evening peak user campaign on a computing device, the instructions when executed by a processor causing the processor to:

aggregate consumption data for a plurality of users, the consumption data corresponding with an amount of energy resource consumption for each of the respective plurality of users;

identify, based on the consumption data, usage patterns for the plurality of users;

identify, from the usage patterns, a subset of the plurality of users that are peak users that consume more resources during a first time period than a second time period;

generate a plurality of use reports for the peak users, wherein each of the use reports provides a visual display of the consumption data for each of the peak users and energy efficiency advice related to consumption during the first time period; and

provide one or more of the use reports to one or more of the peak users to reduce the amount of energy resource consumption for the one or more peak users.