# US 2021/0241392 A1 · Metrics for energy saving and response behavior

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- Number: US 2021/0241392 A1
- Document ID: US20210241392A1
- Status: Published application
- Kind: A1
- Application number: 16/782,723
- Filed: 2020-02-05
- Published: 2021-08-05
- Earliest claimed: 2020-02-05
- Assignees: International Business Machines Corporation
- Applicants: International Business Machines Corporation
- Inventors: Pranshu TIWARI; Harish BHARTI; Saurabh TREHAN; Anshul CHETAL
- Web page: https://patentgrep.com/patent/US20210241392A1

## CPC

- G06Q 50/06: Energy or water supply
- G01R 21/133: by using digital technique
- G06F 16/285: {Clustering or classification}
- G06N 3/084: Backpropagation, e.g. using gradient descent
- G06N 20/00: Machine learning
- G06Q 30/0204: Market segmentation
- G06Q 50/08: Construction
- H02J 13/12: Monitoring network conditions, e.g. electrical magnitudes or operational status
- G06N 3/048: Activation functions
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06N 5/025: Extracting rules from data
- H02J 2105/42: Home appliances
- Y02B 70/30: Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
- Y02B 70/3225: Demand response systems, e.g. load shedding, peak shaving
- Y04S 20/222: Demand response systems, e.g. load shedding, peak shaving
- Y04S 20/242: Home appliances
- Y04S 50/14: Marketing, i.e. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards
- 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
- G01R: MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES (indicating correct tuning of resonant circuits H03J3/12)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- H02J: ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- Y02B: CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y04S: SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS

## Abstract

Methods and systems for metrics for energy saving and response behavior are disclosed. A method includes: receiving, by a computing device, for each of a plurality of energy users, consumption time series data from a smart meter of the energy user; determining, by the computing device, for each of the plurality of energy users, demographic data of the energy user; clustering, by the computing device, the energy users based on the consumption time series data and the demographic data; identifying, by the computing device, a plurality of groups of energy users based upon the clustering; and determining, by the computing device, an energy saving program to associate with each of the plurality of groups.

## Claims

### Claim 1

A method comprising:
receiving, by a computing device, for each of a plurality of energy users, consumption time series data from a smart meter of the energy user;
determining, by the computing device, for each of the plurality of energy users, demographic data of the energy user;
clustering, by the computing device, the energy users based on the consumption time series data and the demographic data;
identifying, by the computing device, a plurality of groups of energy users based upon the clustering; and
determining, by the computing device, an energy saving program to associate with each of the plurality of groups.

### Claim 2 (depends on 1)

The method according to claim 1, wherein the consumption time series data comprises information about appliance-level energy use.

### Claim 3 (depends on 2)

The method according to claim 2, wherein the information about appliance-level energy use is measured by a plug-in power meter.

### Claim 4 (depends on 1)

The method according to claim 1, wherein the clustering comprises using k-clustering to create clusters based on average daily energy use and peak energy use determined using the consumption time series data.

### Claim 5 (depends on 1)

The method according to claim 1, wherein the clustering comprises creating hierarchical clusters using complete linkage and single linkage based on average daily energy use and peak energy use determined using the consumption time series data.

### Claim 6 (depends on 1)

The method according to claim 1, further comprising sending, by the computing device, for each of the plurality of groups, communications regarding the energy saving program to the energy users in the group.

### Claim 7 (depends on 1)

The method according to claim 1, further comprising sending, by the computing device, a recommendation of an architectural change to improve energy efficiency to the energy users in one of the plurality of groups.

### Claim 8

A computer program product comprising:
one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising:
program instructions to receive, for each of a plurality of energy users, consumption time series data from a smart meter of the energy user;
program instructions to cluster the energy users based on the consumption time series data;
program instructions to identify a plurality of groups of energy users based upon the clustering; and
program instructions to send a recommendation of an architectural change to improve energy efficiency to the energy users in one of the plurality of groups.

### Claim 9 (depends on 8)

The computer program product according to claim 8, wherein the consumption time series data comprises information about appliance-level energy use.

### Claim 10 (depends on 9)

The computer program product according to claim 9, wherein the information about appliance-level energy use is measured by a plug-in power meter.

### Claim 11 (depends on 8)

The computer program product according to claim 8, wherein the clustering comprises using k-clustering to create clusters based on average daily energy use and peak energy use determined using the consumption time series data.

### Claim 12 (depends on 8)

The computer program product according to claim 8, wherein the clustering comprises creating hierarchical clusters using complete linkage and single linkage based on average daily energy use and peak energy use determined using the consumption time series data.

### Claim 13 (depends on 8)

The computer program product according to claim 8, further comprising program instructions to send, for each of the plurality of groups, communications regarding energy saving programs to the energy users in each of the plurality of groups.

### Claim 14

A system comprising:
a hardware processor, a computer readable memory, and one or more computer readable storage media associated with a computing device;
program instructions to receive, for each of a plurality of energy users, consumption time series data from a smart meter of the energy user;
program instructions to determine, for each of the plurality of energy users, demographic data of the energy user;
program instructions to cluster the energy users based on the consumption time series data and the demographic data;
program instructions to identify a plurality of groups of energy users based upon the clustering; and
program instructions to determine an energy saving program to associate with each of the plurality of groups,
wherein the program instructions are stored on the one or more computer readable storage media for execution by the hardware processor via the computer readable memory.

### Claim 15 (depends on 14)

The system according to claim 14, wherein the consumption time series data comprises information about appliance-level energy use.

### Claim 16 (depends on 15)

The system according to claim 15, wherein the information about appliance-level energy use is measured by a plug-in power meter.

### Claim 17 (depends on 14)

The system according to claim 14, wherein the clustering comprises using k-clustering to create clusters based on average daily energy use and peak energy use determined using the consumption time series data.

### Claim 18 (depends on 14)

The system according to claim 14, wherein the clustering comprises creating hierarchical clusters using complete linkage and single linkage based on average daily energy use and peak energy use determined using the consumption time series data.

### Claim 19 (depends on 14)

The system according to claim 14, further comprising program instructions to send, for each of the plurality of groups, communications regarding the energy saving program to the energy users in the group.

### Claim 20 (depends on 14)

The system according to claim 14, further comprising program instructions to send a recommendation of an architectural change to improve energy efficiency to the energy users in one of the plurality of groups.