Inference server and environment controller for inferring one or more commands for controlling an appliance taking into account room characteristics

Patent metadata

Field	Value
Publication number	US-11747771-B2
Application number	US-202017116514-A
Country	US
Kind code	B2
Filing date	Dec 9, 2020
Priority date	Dec 12, 2017
Publication date	Sep 5, 2023
Grant date	Sep 5, 2023

Abstract

Inference server and environment controller for inferring via a neural network one or more commands for controlling an appliance. The environment controller determines at least one room characteristic. The environment controller receives at least one environmental characteristic value and at least one set point. The environment controller transmits the at least one environmental characteristic, set point and room characteristic to the inference server. The inference server executes a neural network inference engine using a predictive model (generated by a neural network training engine) for inferring the one or more commands for controlling the appliance. The inference is based on the received at least one environmental characteristic value, at least one set point and at least one room characteristic. The inference server transmits the one or more commands to the environment controller, which forwards the one or more commands to the controlled appliance.

First claim

What is claimed is:

1. An environment controller, comprising:
a communication interface; and
a processing unit for:
receiving at least one room characteristic via one of the communication interface and a user interface of the environment controller, and storing the at least one room characteristic in a memory of the environment controller;
receiving a current temperature via the communication interface;
receiving a target temperature via one of the communication interface and the user interface;
transmitting the at least one room characteristic, the current temperature and the target temperature to an inference server executing a neural network inference engine via the communication interface;
receiving one or more command for controlling an appliance inferred by the neural network inference engine executed by the inference server via the communication interface; and
transmitting the one or more command to the controlled appliance via the communication interface;
wherein the at least one room characteristic comprises at least one of the following: a room type identifier selected among a plurality of room type identifiers, one or more geometric characteristics of the room, and a human activity in the room.

2. The environment controller of claim 1 , wherein the at least one room characteristic comprises one or more geometric characteristics of the room, the one or more geometric characteristics of the room comprising at least one of the following: a volume of the room, a surface of the room, a height of the room, a length of the room, and a width of the room.

3. The environment controller of claim 1 , wherein the at least one room characteristic comprises a human activity in the room, the human activity in the room comprising at least one of the following: periods of time when the room is occupied by humans, and a type of activity performed by humans occupying the room.

4. The environment controller of claim 1 , wherein the processing unit further receives via the communication interface at least one additional environmental characteristic value; and wherein the processing unit transmits the at least one additional environmental characteristic value to the inference server executing the neural network inference engine via the communication interface.

5. The environment controller of claim 4 , wherein the at least one environmental characteristic value comprises at least one of the following: a current humidity level, a current carbon dioxide (CO2) level, and a current room occupancy.

6. The environment controller of claim 4 , wherein the at least one additional environmental characteristic value comprises a current room occupancy, the current room occupancy consisting of a determination whether the room is occupied or not, or consisting of a number of persons present in the room.

7. The environment controller of claim 1 , wherein the processing unit further receives via one of the communication interface and the user interface at least one additional set point; and wherein the processing unit transmits the at least one additional set point to the inference server executing the neural network inference engine via the communication interface.

8. The environment controller of claim 7 , wherein the at least one additional set point comprises at least one of the following: a target humidity level, and a target CO2 level.

9. The environment controller of claim 1 , wherein the one or more command for controlling the appliance includes at least one of the following: a command for controlling a speed of a fan, a command for controlling a pressure generated by a compressor, and a command for controlling a rate of an airflow through a valve.

10. The environment controller of claim 1 , wherein the controlled appliance consists of a Variable Air Volume (VAV) appliance.

11. A method for inferring via a neural network one or more command for controlling an appliance, the method comprising:
determining by a processing unit of an environment controller at least one room characteristic;
receiving by the processing unit a current temperature via a communication interface of the environment controller;
receiving by the processing unit a target temperature via one of the communication interface and a user interface of the environment controller;
transmitting by the processing unit the at least one room characteristic, the current temperature and the target temperature to an inference server executing a neural network inference engine via the communication interface;
receiving by the processing unit the one or more command for controlling the appliance inferred by the neural network inference engine executed by the inference server via the communication interface; and
transmitting by the processing unit the one or more command to the controlled appliance via the communication interface;
wherein the at least one room characteristic comprises at least one of the following: a room type identifier selected among a plurality of room type identifiers, one or more geometric characteristics of the room, and a human activity in the room.

12. The method of claim 11 , wherein the at least one room characteristic comprises one or more geometric characteristics of the room, the one or more geometric characteristics of the room comprising at least one of the following: a volume of the room, a surface of the room, a height of the room, a length of the room, and a width of the room.

13. The method of claim 11 , wherein the at least one room characteristic comprises a human activity in the room, the human activity in the room comprising at least one of the following: periods of time when the room is occupied by humans, and a type of activity performed by humans occupying the room.

14. The method of claim 11 , further comprising receiving by the processing unit via the communication interface at least one additional environmental characteristic value; and transmitting by the processing unit the at least one additional environmental characteristic value to the inference server executing the neural network inference engine via the communication interface.

15. The method of claim 14 , wherein the at least one additional environmental characteristic value comprises at least one of the following: a current humidity level, a current carbon dioxide (CO2) level, and a current room occupancy.

16. The method of claim 14 , wherein the at least one additional environmental characteristic value comprises a current room occupancy, the current room occupancy consisting of a determination whether the room is occupied or not, or consisting of a number of persons present in the room.

17. The method of claim 11 , further comprising receiving by the processing unit via one of the communication interface and the user interface at least one additional set point; and transmitting by the processing unit the at least one additional set point to the inference server executing the neural network inference engine via the communication interface.

18. The method of claim 17 , wherein the at least one additional set point comprises at least one of the following: a target humidity level, and a target CO2 level.

19. The method of claim 11 , wherein the one or more command for controlling the appliance includes at least one of the following: a command for controlling a speed of a fan, a command for controlling a pressure generated by a compressor, and a command for controlling a rate of an airflow through a valve.

20. The method of claim 11 , wherein the controlled appliance consists

Assignees

• Distech Controls Inc

Inventors

• Gervais Francois

Classifications

• G05B13/0265 Primary

  the criterion being a learning criterion · CPC title

• C05B17/02

  containing manganese · CPC title

• F24F11/30

  for purposes related to the operation of the system, e.g. for safety or monitoring · CPC title

• F24F11/62

  characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values · CPC title

• F24F11/76

  by means responsive to temperature, e.g. bimetal springs · CPC title