ENERGY MANAGEMENT SYSTEM AND METHOD

• US 20170285591A1
• Filed: 04/05/2016
• Published: 10/05/2017
• Est. Priority Date: 04/05/2016
• Status: Active Grant

• Current Assignee: Schneider Electric USA, Inc. (Schneider Electric SE)
• Original Assignee: Schneider Electric USA, Inc. (Schneider Electric SE)
• Inventors: Menzel, Johannes
• Granted Patent: US 10,120,353 B2
• Time in Patent Office: Days
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G06F 1/266 Arrangements to supply power to external peripherals either directly from the computer or under computer control, e.g. supply of power through the communication port, computer controlled power-strips
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G06Q 50/06 Energy or water supply

Abstract

A method for energy management include steps of collecting data during a period of time, identifying data clusters within the data, categorizing the data clusters in the period of time, analyzing the data clusters, and assigning a classification to the period of time. Real time data can be compared to scheduled performance. Real time notifications can be provided to a user where energy is being used inefficiently. Energy efficiency of meters within a site can be ranked to identify meters where energy is being used inefficiently. A system is also provided for performing the method of the present disclosure.

Claims

1. A method for energy management, the method comprising:

collecting energy consumption data for a site during a period of time using one or more energy consumption meters;

identifying data clusters within the energy consumption data, the data clusters indicating a status of energy consumption at the site;

categorizing the data clusters in the period of time;

analyzing the data clusters; and

assigning one of a plurality of energy consumption classifications to the period of time.

2. The method of claim 1, wherein the period of time is classified as one of:

an open day, a 24-hour open day, and a 24-hour closed day.

3. The method of claim 1, further comprising:

identifying the period of time as being one of;

a normal period of time and an abnormal period of time, with respect to an actual energy consumption during the period of time.

4. The method of claim 3, further comprising:

controlling at least one device to manage the energy consumption in response to an identification of an abnormal time period.

5. The method of claim 1, further comprising:

providing at least one recommendation to a user to improve energy consumption efficiency for the site.

6. The method of claim 1, further comprising:

providing a desired energy usage and a desired classification for the period of time;

comparing actual energy usage to the desired energy usage; and

comparing the energy consumption classification assigned to the period of time with the desired classification.

7. The method of claim 1, further comprising:

performing a self-learning and continuously self-adapting process for learning a normal energy consumption pattern for the site.

8. The method of claim 1, further comprising:

providing a plurality of meters;

analyzing the data collected from each meter;

ranking the meters according to energy management efficiency;

notifying a user of the meter ranking.

9. The method of claim 1, further comprising:

identifying a first data cluster corresponding to normal running hours;

identifying a second data cluster corresponding to normal closed hours;

calculating a first mean energy consumption value for the first data cluster and a second mean energy consumption value for the second data cluster; and

calculating a ratio of the first mean energy consumption value to the second mean energy consumption value.

10. The method of claim 9, further comprising:

notifying a user if the ratio of the first mean energy consumption value to the second mean energy consumption value is below a critical ratio value.

11. The method of claim 1, further comprising:

identifying a data cluster corresponding to energy consumption transitioning between normal running hours and normal closed hours;

calculating a slope of the data cluster; and

notifying a user if the slope is below a critical slope.

12. A system for energy management, the system comprising:

at least one energy consumption meter;

a controller having a processor and a memory component, the controller being in communication with the at least one energy consumption meter;

wherein the processor is configured to;

collect energy consumption data for a site during a period of time using the at least one energy consumption meter;

identify data clusters within the energy consumption data, the data clusters indicating a status of energy consumption at the site;

categorize the data clusters in the period of time;

analyze the data clusters; and

assign one of a plurality of energy consumption classifications to the period of time.

13. The system of claim 12, wherein the processor is further configured to classify a period of time as one of:

an open day, a 24-hour open day, and a 24-hour closed day.

14. The system of claim 12, wherein the processor is further configured to identify the period of time as being one of:

a normal period of time and an abnormal period of time, with respect to an actual energy consumption during the period of time.

15. The system of claim 14, wherein the processor is further configured to control at least one device to manage the energy consumption in real time in response to an identification of an abnormal time period.

16. The system of claim 12, wherein the processor is further configured to provide a recommendation to a user to improve energy consumption efficiency for the site.

17. The system of claim 12, wherein the processor is further configured to:

provide a desired energy usage and a desired classification for the period of time;

compare actual energy usage to the desired energy usage; and

compare the energy consumption classification assigned to the period of time with the desired classification.

18. The system of claim 12, wherein the processor is further configured to perform a self-learning process for learning a normal energy consumption pattern for the site.

19. The system of claim 12, wherein the processor is further configured to:

analyze the respective data collected from each meter;

rank the meters according to energy efficiency; and

notify a user of the meter ranking.

20. The system of claim 12, wherein the processor is further configured to:

identify a first data cluster corresponding to normal running hours;

identify a second data cluster corresponding to normal closed hours;

calculate a first mean energy consumption value for the first data cluster and a second mean energy consumption value for the second data cluster; and

calculate a ratio of the first mean energy consumption value to the second mean energy consumption value.

21. The system of claim 12, wherein the processor is further configured to:

identify an energy consumption meter that is a main contributors to an abnormal energy consumption