# US 10,310,459 B2 · Electric loadshape forecasting based on smart meter signals

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- Number: US 10,310,459 B2
- Document ID: US10310459B2
- Status: Granted
- Kind: B2
- Application number: 15/715,692
- Filed: 2017-09-26
- Published: 2019-03-28
- Granted: 2019-06-04
- Earliest claimed: 2017-09-26
- Assignees: Oracle International Corporation
- Applicants: Oracle International Corporation
- Inventors: Kenny C. Gross; Mengying Li; Benjamin P. Franklin, Jr.
- Web page: https://patentgrep.com/patent/US10310459B2

## CPC

- G05B 13/048: {using a predictor}
- G05B 13/0265: {the criterion being a learning criterion}
- H02J 3/00: Circuit arrangements for AC mains or AC distribution networks
- H02J 3/003: {Load forecast, e.g. methods or systems for forecasting future load demand}
- H02J 13/12: Monitoring network conditions, e.g. electrical magnitudes or operational status
- H02J 3/38: Arrangements for feeding a single network from two or more generators or sources in parallel; Arrangements for feeding already energised networks from additional generators or sources in parallel
- H02J 2103/30: Simulating, planning, modelling, reliability check or computer assisted design [CAD] of electric power networks
- 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 10/50: Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
- Y04S 40/20: Information technology specific aspects, e.g. CAD, simulation, modelling, system security
- 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)
- H02J: ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- 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

During operation, the system receives a set of input signals containing electrical usage data from a set of smart meters, wherein each smart meter gathers electrical usage data from a customer of the utility system. Next, the system uses the set of input signals to train an inferential model, which learns correlations among the set of input signals, and uses the inferential model to produce a set of inferential signals, wherein an inferential signal is produced for each input signal in the set of input signals. The system then uses a Fourier-based technique to decompose each inferential signal into deterministic and stochastic components, and uses the deterministic and stochastic components to generate a set of synthesized signals, which are statistically indistinguishable from the inferential signals. Finally, the system projects the set of synthesized signals into the future to produce a forecast for the electricity demand.

## Claims

### Claim 1

A method for forecasting electricity demand for a utility system, comprising:
receiving a set of input signals containing electrical usage data from a set of smart meters, wherein each smart meter in the set gathers electrical usage data from a customer of the utility system;
using the set of input signals to train an inferential model, which learns correlations among the set of input signals;
using the inferential model to produce a set of inferential signals, wherein the inferential model produces an inferential signal for each input signal in the set of input signals;
using a Fourier-based decomposition-and-reconstruction technique, which decomposes each signal in the set of inferential signals into deterministic and stochastic components, and uses the deterministic and stochastic components to generate a set of synthesized signals, which are statistically indistinguishable from the inferential signals; and
projecting the set of synthesized signals into the future to produce a forecast for the electricity demand for the utility system; using the forecast for the electricity demand to control a supply of electricity provided by the utility system.

### Claim 2 (depends on 1)

The method of claim 1, wherein controlling the supply of electricity provided by the utility system comprises one or more of the following:
controlling an amount of electricity produced by one or more power plants in the utility system;
purchasing electricity for the utility system through a power grid;
selling electricity produced by the utility system through the power grid;
storing electricity for future use by the utility system; and
making plans to construct a new power plant for the utility system.

### Claim 3 (depends on 1)

The method of claim 1, wherein generating the set of synthesized signals comprises:
generating a set of un-normalized signals; and
performing an ambient-weather-normalization operation on the set of un-normalized signals to generate the set of synthesized signals, wherein the ambient-weather-normalization operation uses historical, current and predicted weather measurements and historical electrical usage data to adjust the set of un-normalized signals to account for effects of weather on the forecast for electricity demand.

### Claim 4 (depends on 1)

The method of claim 1, wherein receiving the set of input signals comprises receiving a set of advanced metering infrastructure (AMI) signals.

### Claim 5 (depends on 1)

The method of claim 1, wherein the inferential model is trained using a nonlinear, nonparametric (NLNP) regression technique.

### Claim 6 (depends on 5)

The method of claim 5, wherein the NLNP regression technique comprises a Multivariate State Estimation Technique (MSET).

### Claim 7 (depends on 1)

The method of claim 1, wherein using the Fourier-based decomposition-and-reconstruction technique to generate the set of synthesized signals comprises using a telemetry parameter synthesis (TPSS) technique, which creates a high-fidelity synthesis equation that is used to generate the set of synthesized signals.

### Claim 8 (depends on 1)

The method of claim 1, wherein the electrical usage data comprises kilowatt measurements and kilowatt-hour measurements.

### Claim 9

A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for forecasting electricity demand for a utility system, the method comprising:
receiving a set of input signals containing electrical usage data from a set of smart meters, wherein each smart meter in the set gathers electrical usage data from a customer of the utility system;
using the set of input signals to train an inferential model, which learns correlations among the set of input signals;
using the inferential model to produce a set of inferential signals, wherein the inferential model produces an inferential signal for each input signal in the set of input signals;
using a Fourier-based decomposition-and-reconstruction technique, which decomposes each signal in the set of inferential signals into deterministic and stochastic components, and uses the deterministic and stochastic components to generate a set of synthesized signals, which are statistically indistinguishable from the inferential signals; and
projecting the set of synthesized signals into the future to produce a forecast for the electricity demand for the utility system using the forecast for the electricity demand to control a supply of electricity provided by the utility system.

### Claim 10 (depends on 9)

The non-transitory computer-readable storage medium of claim 9, wherein controlling the supply of electricity provided by the utility system comprises one or more of the following:
controlling an amount of electricity produced by one or more power plants in the utility system;
purchasing electricity for the utility system through a power grid;
selling electricity produced by the utility system through the power grid;
storing electricity for future use by the utility system; and
making plans to construct a new power plant for the utility system.

### Claim 11 (depends on 9)

The non-transitory computer-readable storage medium of claim 9, wherein generating the set of synthesized signals comprises:
generating a set of un-normalized signals; and
performing an ambient-weather-normalization operation on the set of un-normalized signals to generate the set of synthesized signals, wherein the ambient-weather-normalization operation uses historical, current and predicted weather measurements and historical electrical usage data to adjust the set of un-normalized signals to account for effects of weather on the forecast for electricity demand.

### Claim 12 (depends on 9)

The non-transitory computer-readable storage medium of claim 9, wherein receiving the set of input signals comprises receiving a set of advanced metering infrastructure (AMI) signals.

### Claim 13 (depends on 9)

The non-transitory computer-readable storage medium of claim 9, wherein the inferential model is trained using a nonlinear, nonparametric (NLNP) regression technique.

### Claim 14 (depends on 13)

The non-transitory computer-readable storage medium of claim 13, wherein the NLNP regression technique comprises a Multivariate State Estimation Technique (MSET).

### Claim 15 (depends on 9)

The non-transitory computer-readable storage medium of claim 9, wherein using the Fourier-based decomposition-and-reconstruction technique to generate the set of synthesized signals comprises using a telemetry parameter synthesis (TPSS) technique, which creates a high-fidelity synthesis equation that is used to generate the set of synthesized signals.

### Claim 16 (depends on 9)

The non-transitory computer-readable storage medium of claim 9, wherein the electrical usage data comprises kilowatt measurements and kilowatt-hour measurements.

### Claim 17

A system that forecasts electricity demand for a utility system, comprising:
at least one processor and at least one associated memory; and
a forecasting mechanism that executes on the at least one processor, wherein during operation, the forecasting mechanism:
receives a set of input signals containing electrical usage data from a set of smart meters, wherein each smart meter in the set gathers electrical usage data from a customer of the utility system;
uses the set of input signals to train an inferential model, which learns correlations among the set of input signals;
uses the inferential model to produce a set of inferential signals, wherein the inferential model produces an inferential signal for each input signal in the set of input signals;
uses a Fourier-based decomposition-and-reconstruction technique, which decomposes each signal in the set of inferential signals into deterministic and stochastic components, and uses the deterministic and stochastic components to generate a set of synthesized signals, which are statistically indistinguishable from the inferential signals; and
projects the set of synthesized signals into the future to produce a forecast for the electricity demand for the utility system uses the forecast for the electricity demand to control a supply of electricity provided by the utility system.