# US 10,120,353 B2 · Energy management system and method

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- Number: US 10,120,353 B2
- Document ID: US10120353B2
- Status: Granted
- Kind: B2
- Application number: 15/091,391
- Filed: 2016-04-05
- Published: 2017-10-05
- Granted: 2018-11-06
- Earliest claimed: 2016-04-05
- Assignees: Schneider Electric USA, Inc.
- Applicants: Schneider Electric USA, Inc.
- Inventors: Johannes Menzel
- Web page: https://patentgrep.com/patent/US10120353B2

## CPC

- G05B 15/02: electric
- 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}
- G06Q 10/00: Administration; Management
- G06Q 50/06: Energy or water supply
- G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR

## 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

### Claim 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;
assigning one of a plurality of energy consumption classifications to the period of time;
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;
controlling at least one device to manage the energy consumption in response to an identification of an abnormal period of time;
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.

### Claim 2 (depends on 1)

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.

### Claim 3 (depends on 1)

The method of claim 1, further comprising:
providing at least one recommendation to a user to improve energy consumption efficiency for the site.

### Claim 4 (depends on 1)

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.

### Claim 5 (depends on 1)

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.

### Claim 6 (depends on 1)

The method of claim 1, further comprising:
providing a plurality of energy consumption meters;
analyzing the energy consumption data collected from each energy consumption meter;
ranking the energy consumption meters according to energy management efficiency; and
notifying a user of the energy consumption meter ranking.

### Claim 7 (depends on 1)

The method of claim 1, 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.

### Claim 8 (depends on 1)

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.

### Claim 9

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;
assign one of a plurality of energy consumption classifications to the period of time;
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;
control at least one device to manage the energy consumption in real time in response to an identification of an abnormal period of time;
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.

### Claim 10 (depends on 9)

The system of claim 9, 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.

### Claim 11 (depends on 9)

The system of claim 9, wherein the processor is further configured to provide a recommendation to a user to improve energy consumption efficiency for the site.

### Claim 12 (depends on 9)

The system of claim 9, 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.

### Claim 13 (depends on 9)

The system of claim 9, wherein the processor is further configured to perform a self-learning process for learning a normal energy consumption pattern for the site.

### Claim 14 (depends on 9)

The system of claim 9, wherein the processor is further configured to:
analyze the respective data collected from each energy consumption meter;
rank the energy consumption meters according to energy efficiency; and
notify a user of the energy consumption meter ranking.

### Claim 15 (depends on 9)

The system of claim 9, wherein the processor is further configured to:
identify an energy consumption meter that is a main contributor to an abnormal energy consumption.