Optimizing and controlling the energy consumption of a building

• US 10,354,345 B2
• Filed: 04/04/2017
• Issued: 07/16/2019
• Est. Priority Date: 01/23/2012
• Status: Active Grant

• Current Assignee: Ademco Inc. (Resideo Technologies, Inc.)
• Original Assignee: Whisker Labs, Inc. (Earth Networks Incorporated)
• Inventors: Sloop, Christopher Dale, Siemann, Michael, Gabriel, Steven
• Primary Examiner(s): Ali, Mohammad
• Assistant Examiner(s): Rao, Sheela
• Application Number:
  US15/478,885
• Publication Number:
  US 20170206615A1
• Time in Patent Office: 833 Days
• Field of Search: 700291
• CPC Class Codes: F24F 11/30 for purposes related to the operation of the system, e.g. for safety or monitoring
• CPC Class Codes: F24F 11/47 Responding to energy costs
• CPC Class Codes: F24F 11/52 Indication arrangements, e.g. displays
• CPC Class Codes: F24F 11/58 using Internet communication
• CPC Class Codes: F24F 11/62 characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
• CPC Class Codes: F24F 11/63 Electronic processing
• CPC Class Codes: F24F 11/64 using pre-stored data
• CPC Class Codes: F24F 2110/10 Temperature
• CPC Class Codes: F24F 2110/12 of the outside air
• CPC Class Codes: F24F 2120/00 Control inputs relating to users or occupants
• CPC Class Codes: F24F 2130/00 Control inputs relating to environmental factors not covered by group F24F2110/00
• CPC Class Codes: F24F 2130/10 Weather information or forecasts
• CPC Class Codes: F24F 2140/60 Energy consumption
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05B 2219/2614 HVAC, heating, ventillation, climate control
• CPC Class Codes: G05B 2219/2639 Energy management, use maximum of cheap power, keep peak load low
• CPC Class Codes: G05B 2219/2642 Domotique, domestic, home control, automation, smart house
• CPC Class Codes: G05D 23/1917 using digital means
• CPC Class Codes: G05D 23/1923 using thermal energy, the cost of which varies in function of time
• CPC Class Codes: G06Q 10/06313 Resource planning in a project environment
• CPC Class Codes: G06Q 50/06 : Energy or water supply
• CPC Class Codes: Y02P 90/82 : Energy audits or management systems therefor
• CPC Class Codes: Y04S 20/244 : the home appliances being or involving heating ventilating and air conditioning [HVAC] units

Abstract

Described herein are methods and systems, including computer program products, for determining a load control schedule for energy control devices using a load shifting optimization model and applying the load control schedule to adjust the energy control devices. A server receives thermodynamic models, energy price data and energy load forecast data. The server generates price probability distribution curves based upon the price data and load probability distribution curves based upon the load forecast data. The server executes a load shifting optimization model to determine a profit probability distribution curve for demand response decision rules. The server determines a profit curve that has an optimal profit value. The server generates a load control schedule based upon the optimal profit curve and generates operational parameters for energy control devices using the load control schedule. The server transmits the operational parameters to the energy control devices to adjust operational parameters.

Claims

1. A method for determining a load control schedule for a plurality of energy control devices using a load shifting optimization model and applying the load control schedule to adjust the plurality of energy control devices, the method comprising:

receiving, by a server computing device, one or more thermodynamic grey-box models, wherein each thermodynamic grey-box model is associated with one or more buildings;

receiving, by the server computing device, energy price data associated with a utility provider servicing the one or more buildings and energy load forecast data associated with the one or more buildings;

generating, by the server computing device, one or more price probability distribution curves based upon the energy price data;

generating, by the server computing device, one or more load probability distribution curves based upon the energy load forecast data and the thermodynamic grey-box models;

executing, by the server computing device, a load shifting optimization model using the one or more price probability distribution curves, the one or more load probability distribution curves, and an energy sale price to determine a profit probability distribution curve for each of a plurality of demand response decision rules;

determining, by the server computing device, at least one of the profit probability distribution curves that has an optimal profit value;

generating, by the server computing device, a load control schedule for each of a plurality of energy control devices coupled to the server computing device based upon the profit probability distribution curve that has the optimal profit value;

generating, by the server computing device, one or more operational parameters for each of the plurality of energy control devices using the load control schedule; and

transmitting, by the server computing device, the one or more operational parameters to the plurality of energy control devices, wherein the energy control devices adjust one or more of current and future operational parameters based upon the received operational parameters.

2. The method of claim 1, wherein each thermodynamic grey-box model is based upon one or more characteristics of an indoor environment of a building and a flow of energy through an envelope of the building.

3. The method of claim 1, wherein the energy price data is one or more of historical energy price data and per-hour price probability data.

4. The method of claim 1, wherein the energy load forecast data is one or more of historical load data and load forecast probability data.

5. The method of claim 1, wherein the step of executing the load shifting optimization model comprises:

determining, by the server computing device, a demand response decision rule;

generating, by the server computing device, a vector of simulated market prices using at least one of the price probability distribution curves;

generating, by the server computing device, a vector of simulated loads using at least one of the load probability distribution curves;

applying, by the server computing device, the demand response decision rule to the vector of simulated market prices and the vector of simulated loads to generate the profit probability distribution curve for the demand response decision rule; and

determining, by the server computing device, a profit value associated with the profit probability distribution curve using the energy sale price.

6. The method of claim 5, further comprising repeating, by the server computing device, the execution of the load shifting optimization model for each combination of the plurality of demand response decision rules, the one or more price probability distribution curves, and the one or more load probability distribution curves to generate a plurality of profit values.

7. The method of claim 6, wherein the step of determining at least one of the profit probability distribution curves that has an optimal profit value comprises selecting the profit probability curve that is associated with a maximum profit value.

8. The method of claim 5, further comprising determining, by the server computing device, a risk value associated with the profit probability distribution curve using the energy sale price.

9. The method of claim 8, wherein the step of determining at least one of the profit probability distribution curves that has an optimal profit value comprises selecting the profit probability curve that is associated with a minimum risk value.

10. The method of claim 1, wherein the one or more operational parameters for each of a plurality of energy control devices comprise thermostat setpoints.

11. The method of claim 10, wherein the thermostat setpoints comprise a schedule of current and future temperature settings for the thermostat.

12. The method of claim 1, wherein the one or more operational parameters for each of a plurality of energy control devices comprise operational settings for a comfort device.

13. The method of claim 12, wherein the operational settings for a comfort device comprise a schedule of current and future operational settings for the comfort device.

14. A system for determining a load control schedule for a plurality of energy control devices using a load shifting optimization model and applying the load control schedule to adjust the plurality of energy control devices, the system comprising:

a server computing device that

receives one or more thermodynamic grey-box models, wherein each thermodynamic grey-box model is associated with one or more buildings;

receives energy price data associated with a utility provider servicing the one or more buildings and energy load forecast data associated with the one or more buildings;

generates one or more price probability distribution curves based upon the energy price data;

generates one or more load probability distribution curves based upon the energy load forecast data;

executes a load shifting optimization model using the one or more price probability distribution curves, the one or more load probability distribution curves, and an energy sale price to determine a profit probability distribution curve for each of a plurality of demand response decision rules;

determines at least one of the profit probability distribution curves that has an optimal profit value;

generates a load control schedule for each of a plurality of energy control devices coupled to the server computing device based upon the profit probability distribution curve that has the optimal profit value;

generates one or more operational parameters for each of the plurality of energy control devices using the load control schedule; and

transmits the one or more operational parameters to the plurality of energy control devices, wherein the energy control devices adjust one or more of current and future operational parameters based upon the received operational parameters.

15. The system of claim 14, wherein each thermodynamic grey-box model is based upon one or more characteristics of an indoor environment of a building and a flow of energy through an envelope of the building.

16. The system of claim 14, wherein the energy price data is one or more of historical energy price data and per-hour price probability data.

17. The system of claim 14, wherein the energy load forecast data is one or more of historical load data and load forecast probability data.

18. The system of claim 14, wherein the step of executing the load shifting optimization model comprises:

determining, by the server computing device, a demand response decision rule;

generating, by the server computing device, a vector of simulated market prices using at least one of the price probability distribution curves;

generating, by the server computing device, a vector of simulated loads using at least one of the load probability distribution curves;

applying, by the server computing device, the demand response decision rule to the vector of simulated market prices and the vector of simulated loads to generate the profit probability distribution curve for the demand response decision rule; and

determining, by the server computing device, a profit value associated with the profit probability distribution curve using the energy sale price.

19. The system of claim 18, wherein the server computing device repeats the execution of the load shifting optimization model for each combination of the plurality of demand response decision rules, the one or more price probability distribution curves, and the one or more load probability distribution curves to generate a plurality of profit values.

20. The system of claim 19, wherein determining at least one of the profit probability distribution curves that has an optimal profit value comprises selecting the profit probability curve that is associated with a maximum profit value.

21. The system of claim 18, wherein the server computing device determines a risk value associated with the profit probability distribution curve using the energy sale price.

22. The system of claim 21, wherein determining at least one of the profit probability distribution curves that has an optimal profit value comprises selecting the profit probability curve that is associated with a minimum risk value.

23. The system of claim 14, wherein the one or more operational parameters for each of a plurality of energy control devices comprise thermostat setpoints.

24. The system of claim 23, wherein the thermostat setpoints comprise a schedule of current and future temperature settings for the thermostat.

25. The system of claim 14, wherein the one or more operational parameters for each of a plurality of energy control devices comprise operational settings for a comfort device.

26. The system of claim 25, wherein the operational settings for a comfort device comprise a schedule of current and future operational settings for the comfort device.