# US 10,120,004 B1 · Power consumption analyzing server and power consumption analyzing method thereof

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- Number: US 10,120,004 B1
- Document ID: US10120004B1
- Status: Granted
- Kind: B1
- Application number: 15/814,714
- Filed: 2017-11-16
- Granted: 2018-11-06
- Earliest claimed: 2017-10-20
- Assignees: Institute for Information Industry
- Applicants: Institute For Information Industry
- Inventors: Chia-Wei Tsai; Yung-Chieh Hung; Kuei-Chun Chiang
- Web page: https://patentgrep.com/patent/US10120004B1

## CPC

- G01R 21/1333: {adapted for special tariff measuring}
- G06F 15/00: Digital computers in general (details G06F1/00 – G06F13/00); Data processing equipment in general
- G06F 15/76: Architectures of general purpose stored program computers (with program plugboard G06F15/08; multicomputers G06F15/16)
- 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)
- H02J 3/00: Circuit arrangements for AC mains or AC distribution networks
- H02J 13/12: Monitoring network conditions, e.g. electrical magnitudes or operational status
- G05B 2219/2642: Domotique, domestic, home control, automation, smart house
- G05D 9/00: Level control, e.g. controlling quantity of material stored in vessel
- H02J 3/14: by switching loads on to, or off from, the networks, e.g. progressively balanced loading
- H02J 13/1337: {involving the use of Internet protocols}
- H02J 2105/61: {Load identification}
- H05B 47/155: Coordinated control of two or more light sources
- Y02B 70/3225: Demand response systems, e.g. load shedding, peak shaving
- Y02B 90/20: Smart grids as enabling technology in buildings sector (smart grids supporting the management or operation of end-user stationary applications in general, or like technologies with no associated climate change mitigation effect Y04S20/00)
- Y02E 60/00: Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y04S 10/30: State monitoring, e.g. fault, temperature monitoring, insulator monitoring, corona discharge
- Y04S 20/00: Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S 20/222: Demand response systems, e.g. load shedding, peak shaving
- 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)
- 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
- H02J: ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- 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)
- G05D: SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- H05B: ELECTRIC HEATING; ELECTRIC LIGHT SOURCES NOT OTHERWISE PROVIDED FOR; CIRCUIT ARRANGEMENTS FOR ELECTRIC LIGHT SOURCES, IN GENERAL
- Y02B: CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02E: REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- 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

A power consumption analyzing server and a power consumption analyzing method thereof are provided. According to the user data, the power consumption analyzing server clusters users into a plurality of groups. For each group, the power consumption analyzing server generates M+1 number of encoded outputs by inputting total power consumption time sequence data corresponding to a reference user in the group into an autoencoder. For each group, the power consumption analyzing server receives an actual appliance measurement data from an energy management system apparatus of the reference user, and labels M number of encoded outputs of the M+1 number of encoded outputs to map them to M categories of appliances. Finally, for each group, the power consumption analyzing server establishes a non-intrusive load monitoring system model of the group.

## Claims

### Claim 1

A power consumption analyzing server, comprising:
a network interface;
a storage, being configured to store a user database that records a user datum of each of
a plurality of users;
a processor electrically connected with the network interface and the storage, being configured to execute the following steps:
(a) clustering the users into a plurality of groups according to the user data;
(b) for each of the groups, generating M+1 encoded outputs by inputting a total power consumption time sequence datum of a reference user corresponding to the group among the user data into an autoencoder, where M is a positive integer;
(c) for each of the groups, receiving an actual appliance measurement datum from an energy management system apparatus of the reference user in the group by connecting to the energy management system apparatus via the network interface, the actual appliance measurement datum being associated with M categories of appliances;
(d) for each of the groups, labeling M encoded outputs among the M+1 encoded outputs according to the actual appliance measurement datum to map the M encoded outputs to the M categories of appliances; and
(e) for each of the groups, establishing a non-intrusive load monitoring system (NILM) model of the group according to the M+1 encoded outputs and the actual appliance measurement datum after the M encoded outputs are labeled.

### Claim 2 (depends on 1)

The power consumption analyzing server of claim 1, wherein each of the user data further comprises at least one of: statistical power consumption, a load curve, a building category, a population in a house, a region category, and a climate category.

### Claim 3 (depends on 1)

The power consumption analyzing server of claim 1, wherein the total power consumption time sequence data are measured at a sampling rate of lower than or equal to one sample per minute.

### Claim 4 (depends on 1)

The power consumption analyzing server of claim 1, wherein for a served user among the users, the processor further generates a power consumption suggestion datum according to the non-intrusive load monitoring system model of the group.

### Claim 5 (depends on 1)

The power consumption analyzing server of claim 1, wherein for each of the groups, the processor further corrects the non-intrusive load monitoring system model of the group according to the actual appliance measurement data of the reference user.

### Claim 6 (depends on 1)

The power consumption analyzing server of claim 1, wherein the processor performs one-dimensional, two-dimensional or multi-dimensional clustering according to at least one parameter in the user data to cluster the users into the groups.

### Claim 7 (depends on 1)

The power consumption analyzing server of claim 1, wherein the processor further connects to an electricity data server via the network interface to obtain the user data of the users from the electricity data server.

### Claim 8 (depends on 1)

The power consumption analyzing server of claim 1, wherein the processor further connects to an electricity data server via the network interface to obtain a total power consumption datum of each of the users from the electricity data server, wherein the total power consumption time sequence datum of the reference user is obtained from the total power consumption datum of the reference user by setting an observation window.

### Claim 9 (depends on 8)

The power consumption analyzing server of claim 8, wherein the total power consumption datum is constituted by a plurality of sub-total power consumption time sequence data, and any two of the sub-total power consumption time sequence data that are adjacent in time sequence are partly overlapped or non-overlapped with each other.

### Claim 10 (depends on 1)

The power consumption analyzing server of claim 1, wherein the processor further connects to a plurality of smart electricity meters via the network interface.

### Claim 11

A power consumption analyzing method for a power consumption analyzing server, the power consumption analyzing server comprising a processor, a network interface and a storage, the storage being configured to store a user database that records a user datum of each of a plurality of users, the power consumption analyzing method being executed by the processor and comprising the following steps:
(a) clustering the users into a plurality of groups according to the user data;
(b) for each of the groups, generating M+1 encoded outputs by inputting a total power consumption time sequence datum of a reference user corresponding to the group among the user data into an autoencoder, where M is a positive integer;
(c) for each of the groups, receiving an actual appliance measurement datum from an energy management system apparatus of the reference user in the group by connecting to the energy management system apparatus via the network interface, the actual appliance measurement datum being associated with M categories of appliances;
(d) for each of the groups, labeling M encoded outputs among the M+1 encoded outputs according to the actual appliance measurement datum to map the M encoded outputs to the M categories of appliances; and
(e) for each of the groups, establishing a non-intrusive load monitoring system (NILM) model of the group according to the M+1 encoded outputs and the actual appliance measurement datum after the M encoded outputs are labeled.

### Claim 12 (depends on 11)

The power consumption analyzing method of claim 11, wherein each of the user data further comprises at least one of: statistical power consumption, a load curve, a building category, a population in a house, a region category, and a climate category.

### Claim 13 (depends on 11)

The power consumption analyzing method of claim 11, wherein the total power consumption time sequence data are measured at a sampling rate of lower than or equal to one sample per minute.

### Claim 14 (depends on 11)

The power consumption analyzing method of claim 11, further comprising the following step:
for a served user among the users, generating a power consumption suggestion datum according to the non-intrusive load monitoring system model of the group corresponding to the served user.

### Claim 15 (depends on 11)

The power consumption analyzing method of claim 11, further comprising the following step:
for each of the groups, correcting the non-intrusive load monitoring system model of the group according to the actual appliance measurement data of the reference user.

### Claim 16 (depends on 11)

The power consumption analyzing method of claim 11, wherein the step (a) is to perform one-dimensional, two-dimensional or multi-dimensional clustering according to at least one parameter in the user data to cluster the users into the groups.

### Claim 17 (depends on 11)

The power consumption analyzing method of claim 11, further comprising the following step:
connecting to an electricity data server via the network interface to obtain the user data of the users from the electricity data server.

### Claim 18 (depends on 11)

The power consumption analyzing method of claim 11, wherein the power consumption analyzing server connects to an electricity data server to obtain a total power consumption datum of each of the users from the electricity data server, wherein the total power consumption time sequence datum of the reference user is obtained from the total power consumption datum of the reference user by setting an observation window.

### Claim 19 (depends on 18)

The power consumption analyzing method of claim 18, wherein the total power consumption datum is constituted by a plurality of sub-total power consumption time sequence data, and any two of the sub-total power consumption time sequence data that are adjacent in time sequence are partly overlapped or non-overlapped with each other.

### Claim 20 (depends on 11)

The power consumption analyzing method of claim 11, wherein the power consumption analyzing server connects to a plurality of smart electricity meters.