# US 2021/0240147 A1 · Systems and methods for intervention control in a building management system

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- Number: US 2021/0240147 A1
- Document ID: US20210240147A1
- Status: Published application
- Kind: A1
- Application number: 16/795,197
- Filed: 2020-02-19
- Published: 2021-08-05
- Earliest claimed: 2020-01-31
- Assignees: Johnson Controls Technology Company
- Applicants: Johnson Controls Technology Company
- Inventors: Dimitrios S. Papadopoulos; Kirk H. Drees
- Web page: https://patentgrep.com/patent/US20210240147A1

## CPC

- F24F 11/52: Indication arrangements, e.g. displays
- G05B 15/02: electric
- F24F 11/30: for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F 11/56: Remote control
- F24F 11/58: using Internet communication
- F24F 11/61: using timers
- F24F 11/64: using pre-stored data
- F24F 11/65: for selecting an operating mode
- G05B 13/024: {in which a parameter or coefficient is automatically adjusted to optimise the performance}
- G05B 13/0265: {the criterion being a learning criterion}
- G05B 13/048: {using a predictor}
- G06Q 10/04: Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202)
- G06Q 10/06: Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q 10/0639: Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q 20/102: {Bill distribution or payments}
- G06Q 50/06: Energy or water supply
- G06Q 50/163: Real estate management
- F24F 2110/10: Temperature
- F24F 2110/20: Humidity
- F24F 2120/12: Position of occupants
- F24F 2120/20: Feedback from users
- F24F 2140/60: Energy consumption
- G05B 2219/2614: HVAC, heating, ventillation, climate control
- F24F: AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING (removing dirt or fumes from areas where they are produced B08B15/00; vertical ducts for carrying away waste gases from buildings E04F17/02; tops for chimneys or ventilating shafts, terminals for flues F23L17/02)
- 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)
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR

## Abstract

A method of predicting a time of effect of an intervention of a point of a Building Management System (BMS). The method includes evaluating a first input to determine how an intervention of a point will affect a variable of the BMS; predicting a time at which the intervention will affect the variable of the BMS; presenting feedback to a user via a user interface before implementing the intervention of the point, the feedback comprising the time at which the intervention of the point is predicted to affect the variable; and implementing the intervention or a cancellation of the intervention based at least in part on a second input from the user or an automated response to the feedback. The method allows for users to determine whether to implement proposed interventions in real-time.

## Claims

### Claim 1

A method of predicting a time of effect of an intervention of a point of a Building Management System (BMS), the method comprising:
predicting a time at which an intervention of a point will affect a variable of the BMS;
presenting feedback to a user via a user interface before implementing the intervention of the point, the feedback comprising the time at which the intervention of the point is predicted to affect the variable; and
implementing the intervention or a cancellation of the intervention based at least in part on an input from the user or an automated response to the feedback.

### Claim 2 (depends on 1)

The method of claim 1, further comprising:
obtaining a range of acceptable values of the variable;
wherein predicting the time at which the intervention will affect the variable comprises determining a time at which the intervention will cause a value of the variable to be within the range of acceptable values of the variable.

### Claim 3 (depends on 2)

The method of claim 2, further comprising;
determining a time at which the intervention will cause a second value of the variable to be outside of the range of acceptable values of the variable.

### Claim 4 (depends on 1)

The method of claim 1, wherein predicting the time at which the intervention will affect the variable of the BMS comprises:
obtaining live BMS data associated with operation of the BMS;
providing the live BMS data to one or more machine learning models;
obtaining a confidence score from the one or more machine learning models as to the time at which the intervention will affect the variable of the BMS; and
predicting the time at which the intervention will affect the variable of the BMS based on the confidence score.

### Claim 5 (depends on 4)

The method of claim 4, further comprising:
generating a training dataset using historical data associated with the BMS, the training dataset comprising timeseries with an output indicating a second time that a second intervention will affect the variable; and
training the one or more machine learning models using the training dataset.

### Claim 6 (depends on 4)

The method of claim 4, wherein the one or more machine learning models comprise at least one selected from a group of:
a first machine learning model for predicting control system data if there is not an intervention; and
a second machine learning model for predicting control system data if there is an intervention; wherein predicting a time at which the intervention will affect the variable of the BMS is based on an output from the second machine learning model.

### Claim 7 (depends on 6)

The method of claim 6, wherein the one or more machine learning models further comprise at least one selected from a group of:
a third machine learning model for predicting an effect that external loading disturbances will have on the BMS; and
a fourth machine learning model for predicting an effect that internal loading disturbances will have on the BMS;
wherein each of the third machine learning model and the fourth machine learning model provide outputs to each of the first machine learning model and the second machine learning model.

### Claim 8 (depends on 1)

The method of claim 1, further comprising:
presenting, on the user interface, a time range at which an intervention would provide an occupant with a highest comfortability; or
presenting, on the user interface, a second time range at which an intervention would provide for a highest energy savings or a highest cost savings.

### Claim 9 (depends on 1)

The method of claim 1, further comprising:
presenting, on the user interface, first values of the variable over time if the user does not input the intervention and second values of the variable over time if the user does input the intervention.

### Claim 10 (depends on 1)

The method of claim 1, wherein the variable is an energy consumption, a zone air temperature, a zone humidity, or a discharge air temperature.

### Claim 11 (depends on 1)

The method of claim 1, wherein the point is a supply vent state, a supply fan state, a discharge air temperature setpoint, a zone air temperature setpoint, a ductwork damper state, or a cooling coil valve position.

### Claim 12

A system comprising one or more memory devices storing instructions thereon that, when executed by one or more processors, cause the one or more processors to:
receive a first input related to an intervention of a point;
predict a time at which the intervention will affect a variable of the system; and
implement the intervention or a cancellation of the intervention based at least in part on the time.

### Claim 13 (depends on 12)

The system of claim 12, wherein the instructions cause the one or more processors to further:
obtain a range of acceptable values of the variable;
wherein predicting the time at which the intervention will affect the variable comprises determining a time at which the intervention will cause a value of the variable to be within the range of acceptable values of the variable.

### Claim 14 (depends on 13)

The system of claim 13, wherein the instructions cause the one or more processors to further:
determine a time at which the intervention will cause a second value of the variable to be outside of the range of acceptable values of the variable.

### Claim 15 (depends on 12)

The system of claim 12, wherein the instructions cause the one or more processors to predict the time at which the intervention will affect the variable of the system comprises:
obtaining live system data associated with operation of the system;
providing the live system data to one or more machine learning models;
obtaining a confidence score from the one or more machine learning models as to the time at which the intervention will affect the variable of the system; and
predicting the time at which the intervention will affect the variable of the system based on the confidence score.

### Claim 16 (depends on 15)

The system of claim 15, wherein the instructions cause the one or more processors to further:
generate a training dataset using historical data associated with the system, the training dataset comprising timeseries with an output indicating a second time at which a second intervention will affect the variable; and
train the one or more machine learning models using the training dataset.

### Claim 17

A method for predicting a time of effect of an intervention of a point of a Building Management System (BMS), the method comprising:
predicting a time at which an intervention of a point will cause a value of a variable to be outside of an acceptable range of values;
presenting feedback to a user via a user interface before implementing the intervention of the point, the feedback comprising the time at which the intervention will cause the value of the variable to be outside of the acceptable range of values; and
implementing the intervention, a modification of the intervention, or a cancellation of the intervention based at least in part on an input from the user or an automated response to the feedback.

### Claim 18 (depends on 17)

The method of claim 17, wherein predicting the time at which the intervention will affect the variable of the BMS comprises:
obtaining live BMS data associated with operation of the BMS;
providing the live BMS data to one or more machine learning models;
obtaining a confidence score from the one or more machine learning models as to the time at which the intervention cause the value of the variable to be outside of the acceptable range of values; and
predicting the time at which the intervention will cause the value of the variable to be outside of the acceptable range of values based on the confidence score.

### Claim 19 (depends on 18)

The method of claim 18, wherein the one or more machine learning models comprise at least one selected from a group of:
a first machine learning model for predicting control system data if there is not an intervention; and
a second machine learning model for predicting control system data if there is an intervention; wherein predicting a time at which the intervention will affect the variable of the BMS is based on an output from the second machine learning model.

### Claim 20 (depends on 19)

The method of claim 19, wherein the one or more machine learning models further comprise at least one selected from a group of:
a third machine learning model for predicting an effect that external loading disturbances will have on the BMS; and
a fourth machine learning model for predicting an effect that internal loading disturbances will have on the BMS;
wherein each of the third machine learning model and the fourth machine learning model provide outputs to each of the first machine learning model and the second machine learning model.