Building control system with heat disturbance estimation and prediction
An environmental control system for a building including heating, ventilation, or air conditioning (HVAC) equipment that operates to affect a zone of the building and a controller including a processing circuit. The processing circuit is configured to estimate a thermal resistance between air of the zone and of an external space using values of a temperature of the zone air, a temperature of the external space air, and a heat transfer rate of the HVAC equipment, each value corresponding to a different time step within a time period. The processing circuit is configured to use the thermal resistance, time step specific values of the temperatures, and time step specific values of the heat transfer rate to estimate corresponding values of a heat disturbance. The processing circuit is configured to operate the HVAC equipment using a model-based control technique based on the heat disturbance values.
1. An environmental control system for a building, the system comprising:
heating, ventilation, or air conditioning (HVAC) equipment that operates to affect a variable state or condition of a zone of the building; and
a controller comprising a processing circuit configured to:
estimate a thermal resistance between air of the zone and air of an external space outside the zone using a plurality of values of a temperature of the air of the zone, a temperature of the air of the external space, and a heat transfer rate of the HVAC equipment that provides heating or cooling to the zone, each of the plurality of values corresponding to a different time step within a time period;
for a plurality of time steps within the time period, use the thermal resistance between the air of the zone and the air of the external space, a time step specific value of the temperature of the air of the zone, a time step specific value of the temperature of the air of the external space, and a time step specific value of the heat transfer rate of the HVAC equipment to estimate a corresponding time step specific value of a heat disturbance; and
operate the HVAC equipment over the time period using a model-based control technique based on the time step specific values of the heat disturbance.
2. The system of claim 1 , wherein the time period is a historical time period, and wherein the processing circuit is further configured to perform a system identification process based on the time step specific values of the heat disturbance for the historical time period to identify a predictive model configured to predict future time step specific values of the heat disturbance, wherein the predictive model is used in the model-based control technique.
3. The system of claim 2 , wherein the system identification process comprises identifying a scaling parameter that scales the future time step specific values of the heat disturbance.
4. The system of claim 1 , wherein the processing circuit is configured to identify a Kalman gain and a stochastic model of the heat disturbance based on the time step specific values of the heat disturbance or predicted heat disturbance values based on the time step specific values of the heat disturbance, the Kalman gain and the stochastic model used in the model-based control technique.
5. The system of claim 4 , wherein identifying the Kalman gain and the stochastic model comprises performing a multi-step system identification process.
6. The system of claim 1 , wherein the processing circuit is configured to filter the time step specific values of the heat disturbance through at least one of:
an anti-spike filter; or
a smoothing filter.
7. The system of claim 1 , wherein the processing circuit is configured to generate a predictive model configured to predict future values of the heat disturbance by fitting the time step specific values of the heat disturbance to at least one of:
a Gaussian function;
a sinusoid function; or
a user-defined function.
8. A method for operating heating, ventilation, or air conditioning (HVAC) equipment of a building, the method comprising:
estimating a thermal resistance between air of a zone of the building and air of an external space outside the zone using a plurality of values of a temperature of the air of the zone, a temperature of the air of the external space, and a heat transfer rate of the HVAC equipment that provides heating or cooling to the zone, each of the plurality of values corresponding to a different time step within a time period;
for a plurality of time steps within the time period, using the thermal resistance between the air of the zone and the air of the external space, a time step specific value of the temperature of the air of the zone, a time step specific value of the temperature of the air of the external space, and a time step specific value of the heat transfer rate of the HVAC equipment to estimate a corresponding time step specific value of a heat disturbance; and
operating the HVAC equipment over the time period using a model-based control technique based on the time step specific values of the heat disturbance.
9. The method of claim 8 , wherein the time period is a historical time period, the method further comprising performing a system identification process based on the time step specific values of the heat disturbance for the historical time period to identify a predictive model configured to predict future time step specific values of the heat disturbance, wherein the predictive model is used in the model-based control technique.
10. The method of claim 9 , wherein the system identification process comprises identifying a scaling parameter that scales the future time step specific values of the heat disturbance.
11. The method of claim 8 , further comprising identifying a Kalman gain and a stochastic model of the heat disturbance based on the time step specific values of the heat disturbance or predicted heat disturbance values based on the time step specific values of the heat disturbance, the Kalman gain and the stochastic model used in the model-based control technique.
12. The method of claim 11 , wherein identifying the Kalman gain and the stochastic model comprises performing a multi-step system identification process.
13. The method of claim 8 , further comprising filtering the time step specific values of the heat disturbance through at least one of:
an anti-spike filter; or
a smoothing filter.
14. The method of claim 8 , further comprising generating a predictive model configured to predict future time step specific values of the heat disturbance by fitting the time step specific values of the heat disturbance to at least one of:
a Gaussian function;
a sinusoid function; or
a user-defined function.
15. A controller for operating heating, ventilation, or air conditioning (HVAC) equipment of a building, the controller comprising:
one or more processors; and
one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
estimating a thermal resistance between air of a zone of the building and air of an external space outside the zone using a plurality of values of a temperature of the air of the zone, a temperature of the air of the external space, and a heat transfer rate of the HVAC equipment that provides heating or cooling to the zone, each of the plurality of values corresponding to a different time step within a time period;
for a plurality of time steps within the time period, using the thermal resistance between the air of the zone and the air of the external space, a time step specific value of the temperature of the air of the zone, a time step specific value of the temperature of the air of the external space, and a time step specific value of the heat transfer rate of the HVAC equipment to estimate a corresponding time step specific value of a heat disturbance; and
operating the HVAC equipment over the time period using a model-based control technique based on the time step specific values of the heat disturbance.
16. The controller of claim 15 , wherein the time period is a historical time period, the operations further comprising performing a system identification process based on the time step specific values of the heat disturbance for the historical time period to identify a predictive model configured to predict future time step specific values of the heat disturbance, wherein the predictive model is used in the model-based control technique.
17. The controller of claim 16 , wherein the system identification process comprises identifying a scaling parameter that scales the future time step specific values of the heat disturbance.
18. The controller of claim 15 , the operations further comprising performing a multi-step system identification process based on the time step specific values of the heat disturbance or predicted heat disturbance values based on the time step specific values of the heat disturbance to identify a Kalman gain and a stochastic model of the heat disturbance, the Kalman gain and the stochastic model used in the model-based control technique.
19. The controller of claim 15 , the operations further comprising filtering the time step specific values of the heat disturbance through at least one of:
an anti-spike filter; or
a smoothing filter.
20. The controller of claim 15 , the operations further comprising generating a predictive model configured to predict future time step specific values of the heat disturbance by fitting the time step specific values of the heat disturbance to at least one of:
a Gaussian function;
a sinusoid function; or
a user-defined function.