Granted Patent B2

US 12,401,195 · App. 18/622,673 · Granted Aug 26, 2025

Inventors: Nathan Walkingshaw (Sandy, UT); David Duane Rackham (Lehi, UT); Dalya Adams (Baton Rouge, LA); Jackson Bennett (Washington, DC)

Assignee: Torus Inc.

H02J3/003G01W1/10H02J2203/20

Abstract

Claims (75)

1. A computer-implemented method comprising:

receiving, by one or more processors, a set of power consumption data points for one or more nodes, the one or more nodes including one or more meters for determining at least one data point of the set of power consumption data points, each of the one or more nodes including an energy storage unit, the energy storage unit including one or more of a mechanical battery and a chemical battery;

determining, by the one or more processors, a first subset and a second subset of the set of power consumption data points for the one or more nodes, the first subset corresponding to data points from one or more devices classified as controllable, the second subset corresponding to one or more devices classified as not controllable and indicating a baseline power consumption for the one or more nodes, the second subset of the set of power consumption data points omitting data points from one or more controllable loads from which the set of power consumption data points is determined;

training, by the one or more processors, a first machine learning model to predict a future power consumption using the baseline power consumption for the one or more nodes;

receiving, by the one or more processors, context data for the one or more nodes, the context data including local weather data and weather forecast data for the one or more nodes;

receiving, by the one or more processors, local power production data for the one or more nodes, the local power production data being determined for one or more solar panels electrically coupled with the energy storage unit;

training, by the one or more processors, a second machine learning model using the local power production data and the local weather data;

computing, by the one or more processors, a predicted power differential at a future time for the one or more nodes based on the weather forecast data, the first machine learning model, and the second machine learning model;

controlling, by the one or more processors, a mode of the one or more nodes based on the predicted power differential at the future time, the mode defining whether the one or more nodes perform one or more of receiving, storing, or outputting electrical power; and

directing, by the one or more processors, power consumption of the one or more controllable loads of the one or more nodes based on the predicted power differential at the future time.

2. A computer-implemented method comprising:

receiving, by one or more processors, a set of power consumption data points for a node;

determining, by the one or more processors, a first subset and a second subset of the set of power consumption data points for the node, the first subset corresponding to data points from one or more devices classified as controllable, the second subset corresponding to one or more devices classified as not controllable and indicating a baseline power consumption for the node;

determining, by the one or more processors, a consumption model for the node using the baseline power consumption for the node, the consumption model predicting a future power consumption for the node;

receiving, by the one or more processors, context data for the node, the context data including weather forecast data for the node;

determining, by the one or more processors, a production model for the node, the production model predicting a future power production of the node using the weather forecast data for the node;

computing, by the one or more processors, a predicted power differential at a future time for the node based on the predicted power consumption, the predicted power production, and the context data for the node; and

performing, by the one or more processors, one or more automated operations using the predicted power differential and based on the baseline power consumption for the node, the one or more automated operations including transmitting a control instruction for at least one of the one or more devices classified as controllable.

3. The computer-implemented method of claim 2 , wherein:

the consumption model for the node includes a first machine learning model trained on power consumption data of one or more loads electrically coupled with an energy storage unit of the node.

4. The computer-implemented method of claim 2 , further comprising:

receiving, by the one or more processors, the set of power consumption data points for the node, the node including one or more meters for determining at least one data point of the set of power consumption data points;

determining, by the one or more processors, the baseline power consumption for the node using the second subset of the set of power consumption data points; and

training, by the one or more processors, a first machine learning model to predict the future power consumption based on the second subset of the set of power consumption data points.

5. The computer-implemented method of claim 4 , wherein:

the second subset of the set of power consumption data points omits data points from one or more controllable loads from which the set of power consumption data points is determined.

6. The computer-implemented method of claim 2 , wherein:

the context data includes local weather data received from a local power station to the node, the local weather data informing one or more of the consumption model and the production model for the node.

7. The computer-implemented method of claim 6 , further comprising:

receiving, by the one or more processors, local power production data for the node; and

training, by the one or more processors, a second machine learning model using the local power production data and the local weather data, the production model including the second machine learning model.

8. The computer-implemented method of claim 2 , wherein:

the production model includes a second machine learning model trained using power production and context data for one or more second nodes, the one or more second nodes having one or more attributes in common with the node.

9. The computer-implemented method of claim 2 , further comprising:

aggregating, by the one or more processors, predicted power differentials for a plurality of nodes, the predicted power differentials including the computed power differential for the node;

generating, by the one or more processors, one or more analytics based on the predicted power differentials for the plurality of nodes; and

providing, by the one or more processors, one or more graphical user interfaces graphically showing the one or more analytics.

10. The computer-implemented method of claim 2 , wherein performing the one or more automated operations using the predicted power differential includes:

controlling, by the one or more processors, a mode of the node based on the predicted power differential at the future time, the mode defining whether the node performs one or more of receiving, storing, or outputting electrical power.

11. The computer-implemented method of claim 2 , wherein performing the one or more automated operations using the predicted power differential includes:

directing, by the one or more processors, power consumption of one or more controllable loads of the node based on the predicted power differential.

12. A system comprising:

an energy-storage unit including:

a battery storing energy mechanically or chemically;

an inverter coupled with the battery and converting direct current from the battery to alternating current; and

a controller communicatively coupled with the inverter, the controller controlling one or more functions of the battery; and

one or more processors executing instructions that cause the one or more processors to perform operations comprising:

receiving a set of power consumption data points for a node;

determining a first subset and a second subset of the set of power consumption data points for the node, the first subset corresponding to data points from one or more devices classified as controllable, the second subset corresponding to one or more devices classified as not controllable and indicating a baseline power consumption for the node;

determining a consumption model for the node using the baseline power consumption for the node, the consumption model predicting a future power consumption for the node;

receiving context data for the node, the context data including weather forecast data for the node;

determining a production model for the node, the production model predicting a future power production of the node using the weather forecast data for the node;

computing a predicted power differential at a future time for the node based on the predicted power consumption, the predicted power production, and the context data for the node; and

performing one or more automated operations using the predicted power differential and based on the baseline power consumption for the node, the one or more automated operations including transmitting a control instruction for at least one of the one or more devices classified as controllable.

13. The system of claim 12 , wherein:

the consumption model for the node includes a first machine learning model trained on power consumption data of one or more loads electrically coupled with an energy storage unit of the node.

14. The system of claim 12 , wherein the operations further comprise:

receiving the set of power consumption data points for the node, the node including one or more meters for determining at least one data point of the set of power consumption data points;

determining the baseline power consumption for the node using the second subset of the set of power consumption data points; and

training a first machine learning model to predict the future power consumption based on the second subset of the set of power consumption data points.

15. The system of claim 14 , wherein:

the second subset of the set of power consumption data points omits data points from one or more controllable loads from which the set of power consumption data points are determined.

16. The system of claim 12 , wherein:

the context data includes local weather data received from a local power station to the node, the local weather data informing one or more of the consumption model and the production model for the node.

17. The system of claim 16 , wherein the operations further comprise:

receiving local power production data for the node; and

training a second machine learning model using the local power production data and the local weather data, the production model including the second machine learning model.

18. The system of claim 12 , wherein the operations further comprise:

aggregating predicted power differentials for a plurality of nodes, the predicted power differentials including the computed power differential for the node;

generating one or more analytics based on the predicted power differentials for the plurality of nodes; and

providing one or more graphical user interfaces graphically showing the one or more analytics.

19. The system of claim 12 , wherein performing the one or more automated operations using the predicted power differential includes:

controlling a mode of the node based on the predicted power differential at the future time, the mode defining whether the node performs one or more of receiving, storing, or outputting electrical power.

20. The system of claim 12 , wherein performing the one or more automated operations using the predicted power differential includes:

directing power consumption of one or more controllable loads of the node based on the predicted power differential.

Assignments (1)

Continuity (2)

References Cited (29)