IP Library Granted Patent US 12,241,649
Granted Patent B2
US 12,241,649 · App. 17/591,005 · Granted Mar 4, 2025

MPC for HVAC with thermal model selection

Inventor: Thomas William Dupuis Stesco (Toronto, CA)
Assignee: GENERAC POWER SYSTEMS, INC.
F24F11/64F24F11/38F24F11/47G05B13/04G05B15/02F24F2140/60
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Quick Facts
Patent No.
US 12,241,649
App. No.
17/591,005
Granted
Mar 4, 2025
Kind
B2
Abstract

An example control device includes memory storing a first thermal model of a building and a second thermal model of the building, and a processor connected to the memory. The processor is configured to select the first thermal model or the second thermal model as a selected thermal model, and execute model predictive control (MPC) using the selected thermal model to control a heating, cooling, ventilation, and/or air conditioning (HVAC) apparatus at the building.

Claims (32)

1. A device comprising:

memory storing a first thermal model of a building and a second thermal model of the building;

a processor connected to the memory, the processor configured to:

obtain an operational parameter of a heating, cooling, ventilation, and/or air conditioning (HVAC) apparatus at the building;

compare the operational parameter to a threshold;

when the operational parameter exceeds the threshold, select the first thermal model as a selected thermal model;

when the operational parameter does not exceed the threshold, select the second thermal model as the selected thermal model; and

execute model predictive control using the selected thermal model to control the HVAC apparatus at the building;

determine an error between a sensed HVAC condition of the building and a predicted HVAC condition predicted by the selected thermal model; and

when the error exceeds an error tolerance, disable the model predictive control and enable a reactive control algorithm to control the HVAC apparatus.

2. The device of claim 1 , wherein the operational parameter is a setpoint of the HVAC apparatus.

3. The device of claim 1 , wherein the operational parameter is a mode of the HVAC apparatus.

4. The device of claim 1 , wherein the operational parameter is a time.

5. The device of claim 1 , wherein the first thermal model and the second thermal model are linear models.

6. The device of claim 1 , wherein each of the first and second thermal models model:

a building envelope;

an operational output of the HVAC apparatus; and

a disturbance.

7. The device of claim 6 , wherein the disturbance comprises solar heating, wind, a building occupant, appliance heat generation, or a door being opened or closed.

8. The device of claim 6 , wherein each of the first and second thermal models further models a thermal mass of furniture in the building.

9. The device of claim 1 , wherein the processor is further configured to generate the first thermal model and the second thermal model.

10. The device of claim 1 , wherein the processor is further configured to:

generate a non-linear thermal model of the building; and

perform a piecewise segmentation on the non-linear thermal model to generate the first and second thermal models.

11. The device of claim 10 , wherein the processor is further configured to generate the non-linear thermal model of the building during a training period of operation of the HVAC apparatus.

12. The device of claim 1 , wherein the model predictive control is configured with a comfort constraint and an objective to minimize an operating cost of the HVAC apparatus due to a price of energy.

13. The device of claim 12 further comprising a network interface connected to the processor, the network interface to receive the price of energy from a computer network.

14. The device of claim 1 , wherein the model predictive control is configured with a comfort constraint and an objective to minimize an operating cycle frequency of the HVAC apparatus.

15. The device of claim 1 , wherein the processor is configured to execute the model predictive control at predetermined intervals.

16. The device of claim 1 , further comprising a housing containing the memory and the processor.

17. The device of claim 16 , wherein the housing is configured to be attached to a wall of the building or be placed on a surface inside the building.

18. The device of claim 1 , further comprising a network interface connected to the processor, wherein the processor is configured to execute the model predictive control irrespective of an availability of a network connection at the network interface.

Assignments (6)
CHANGE OF NAME Recorded Nov 3, 2022
From: 1339416 B.C. LTD.
To: ECOBEE TECHNOLOGIES ULC
Reel/Frame 061638/0405 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 3, 2022
From: ECOBEE TECHNOLOGIES ULC
To: GENERAC POWER SYSTEMS, INC.
Reel/Frame 061638/0407 →
CERTIFICATE OF CONTINUANCE Recorded Nov 3, 2022
From: ECOBEE INC.
To: ECOBEE TECHNOLOGIES INC.
Reel/Frame 061871/0164 →
CERTIFICATE OF AMALGAMATION Recorded Nov 3, 2022
From: ECOBEE TECHNOLOGIES INC.
To: 1339416 B.C. LTD.
Reel/Frame 061871/0166 →
SECURITY INTEREST Recorded Sep 19, 2022
From: GENERAC POWER SYSTEMS, INC.
To: JPMORGAN CHASE BANK, N.A., AS ADMINISTRATIVE AGENT
Reel/Frame 061476/0745 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 27, 2022
From: STESCO, THOMAS WILLIAM DUPUIS
To: ECOBEE INC.
Reel/Frame 060642/0331 →
Continuity (1)
Related Publication 20230243541A1 · Aug 3, 2023
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