Systems and methods for estimating a return time

Patent metadata

Field	Value
Publication number	US-RE48574-E
Application number	US-201916548552-A
Country	US
Kind code	E1
Filing date	Aug 22, 2019
Priority date	Dec 16, 2013
Publication date	Jun 1, 2021
Grant date	Jun 1, 2021

Abstract

Systems and methods for estimating a time to cool down or warm up a building zone from a temperature setback condition are provided. A described method includes determining, by a controller for the building zone, at least one of a cooling demand for the building zone and a heating demand for the building zone for a time period corresponding to the temperature setback condition. The method further includes estimating a return time using at least one of the cooling demand and the heating demand. The return time is the time to cool down or warm up the building zone from the temperature setback condition.

First claim

What is claimed is:

1. A method for estimating a time to cool down or warm up a building zone from a temperature setback condition, the method comprising:
monitoring an output signal from a controller for the building zone during a time period corresponding to the temperature setback condition, wherein the output signal is provided from the controller to HVAC equipment that operate operates to cool down or warm up the building zone;
using the output signal from the controller to determine, by the controller for the building zone, at least one of a cooling demand for the building zone and a heating demand for the building zone for the time period corresponding to the temperature setback condition;
estimating a return time using at least one of the cooling demand and the heating demand, wherein the return time is the time to cool down or warm up the building zone from the temperature setback condition;
calculating an estimated deviation of a return time prediction error using a difference between the estimated return time and an actual return time;
calculating a correction factor based on a user selection and the estimated deviation of the return time prediction error, wherein the user selection defines a multiplier for the estimated deviation of the return time prediction error;
using the estimated return time to transition into a cool down state or a warm up state prior to a time of next scheduled occupancy for the building zone; and
operating the HVAC equipment to cool down the building zone in the cool down state or warm up the building zone in the warm up state.

2. The method of claim 1 , further comprising:
identifying a current temperature of the building zone; and
estimating the return time using the current temperature of the building zone and at least one of the cooling demand and the heating demand.

3. The method of claim 1 , further comprising:
comparing the estimated return time with a difference between a current time and a time of next scheduled occupancy for the building zone; and
transitioning from an unoccupied state into at least one of the cool down state and the warm up state in response to the estimated return time being greater than or equal to the difference between the current time and the time of next scheduled occupancy.

4. The method of claim 1 , wherein determining at least one of the cooling demand and the heating demand comprises:
filtering the output signal from the controller using a signal filter to determine at least one of the cooling demand and the heating demand, wherein at least one of the cooling demand and the heating demand is a function of the output signal.

5. The method of claim 4 , wherein the signal filter is at least one of: an analog filter, a digital filter, a low pass filter, a band pass filter, a smoothing filter, a time window filter, a normalizing filter, and an averaging filter; and
wherein the function of the output signal from the controller is at least one of: a last value of the controller output signal, an average of the controller output signal, a normalized value of the controller output signal, an integral of the controller output signal, and a transformation of the output signal from the controller.

6. The method of claim 4 , wherein at least one of the cooling demand and the heating demand is an exponentially weighted moving average based on the output signal from the controller for at least a portion of the time period corresponding to the temperature setback condition.

7. The method of claim 1 , wherein determining at least one of the cooling demand and the heating demand comprises:
identifying the output signal from the controller for the building zone for at least a portion of the time period corresponding to the temperature setback condition;
calculating a normalized controller output by comparing the output signal from the controller with a controller output that provides maximum cooling or maximum heating for the building zone; and
determining an exponentially weighted moving average of the normalized controller output using the calculated normalized controller output and a normalized controller output for a previous sampling time.

8. The method of claim 1 , wherein the return time is estimated using an empirical model having one or more model parameters learned from previous data, wherein the estimated return time is a function of the one or more learned model parameters.

9. The method of claim 8 , further comprising:
initializing values for the one or more model parameters to provide default parameters for the empirical model, wherein at least one of the model parameters is initialized to a non-zero value.

10. The method of claim 8 , wherein the empirical model is at least one of: a statistical model, a parametric model, a regression model, a neural network model, a state space model, and a fuzzy logic model.

11. The method of claim 1 , further comprising:
determining the return time prediction error, wherein the return time prediction error is a difference between the estimated return time and the actual return time;
estimating the deviation of the return time prediction error using a plurality of return time prediction errors; and
correcting the estimated return time by adding a function of the estimated deviation to the estimated return time.

12. The method of claim 11 , further comprising:
determining the multiplier for the estimated deviation of the return time prediction error, wherein the multiplier is based on a probability of achieving an occupied setpoint temperature at a time of occupancy; and
calculating the function of the estimated deviation of the return time prediction error by multiplying the estimated deviation of the return time prediction error by the determined multiplier.

13. The method of claim 1 , further comprising:
receiving the user selection between a level of energy savings and a level of comfort for the building zone, wherein the user selection corresponds to a probability of achieving an occupied setpoint temperature at a time of occupancy;
calculating the correction factor based on the user selection; and
adjusting the estimated return time by applying the correction factor to the estimated return time.

14. The method of claim 1 , further comprising:
comparing a measured temperature of the building zone with an offset temperature setpoint, wherein the offset temperature setpoint is at least one of a heating setpoint minus a temperature offset and a cooling setpoint plus the temperature offset;
updating learned model parameters of an empirical model for estimating the return time in response to at least one of: the measured temperature of the building zone being less than the heating setpoint minus the temperature offset, and the measured temperature of the building zone being greater than the cooling setpoint plus the temperature offset.

15. The method of claim 14 , wherein the measured temperature of the building zone that is compared with the offset temperature setpoint is at least one of: a temperature of the building zone measured at a beginning of a warm up period, and a temperature of the building zone measured at a beginning of a cool down period.

16. The method of claim 14 , wherein updating the learned model parameters of the empirical model comprises:
calculating updated model parameters using at least one of: partial least squares regression, ridge regression, principal component regression, weighted least squares regression, ordinary least squares regression, least mean linear regression, and exponentially weighted regularized least squares regres

Assignees

• Johnson Controls Tech Co

Inventors

• Seem John E
• House John M

Classifications

• F24F11/64 Primary

  using pre-stored data · CPC title

• F24F11/30 Primary

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

• F24F2120/10

  Occupancy · CPC title

• F24F11/61

  using timers · CPC title

• G06N20/00

  Machine learning · CPC title