Hvac controller parameter determination based on signal stabilization

Abstract

Methods and systems for generating parameters and loading them into an HVAC controller are described herein. Setpoint functions are input into a simulation of an environment of the controller. An output of the controller within the simulation is evaluated, using an objective function based on oscillations of the output of the controller, to determine the parameters, which are then loaded into the controller. Alternatively or additionally, data corresponding to an environment in which the controller is implemented is received. The data includes information about a setpoint signal received from an external controller and feedback from a physical system controlled by the controller. The data is evaluated using a reward function to determine parameters, which are then loaded into, or used to augment existing parameters within, the controller. By using the techniques herein, the controller may be configured to stabilize the setpoint signal received from the external controller.

What is claimed is: 
     
         1 . A method of generating one or more parameters for a heating, ventilation, and air conditioning (HVAC) controller, the method comprising:
 receiving one or more setpoint functions;   inputting the setpoint functions into a simulation of an HVAC controller environment;   evaluating an objective function on the simulation of the HVAC controller environment, the objective function being based on oscillations of an output of the HVAC controller in the simulation;   determining, based on the evaluating the objective function, the parameters for the HVAC controller; and   loading the parameters into the HVAC controller.   
     
     
         2 . The method of  claim 1 , wherein the parameters are configured to stabilize the output of the HVAC controller. 
     
     
         3 . The method of  claim 1 , wherein the setpoint functions are setpoint versus time functions that are heuristically derived. 
     
     
         4 . The method of  claim 1 , further comprising deriving the setpoint functions based on historical data, wherein the setpoint functions are setpoint versus time functions. 
     
     
         5 . The method of  claim 4 , wherein each of the setpoint functions corresponds to a cluster of the historical data. 
     
     
         6 . The method of  claim 1 , further comprising receiving historical sensor data, wherein a physical system within the simulation is based on the historical sensor data. 
     
     
         7 . The method of  claim 1 , wherein the determining the parameters comprises using at least one of: a Bayesian Optimization, a Nelder-Mead Optimization, or a Machine Learning Technique. 
     
     
         8 . The method of  claim 1 , wherein:
 the objective function comprises a plurality of weighted terms; and   at least one of the weighted terms corresponds to the oscillations of the output of the HVAC controller.   
     
     
         9 . The method of  claim 8 , wherein another of the weighted terms corresponds to direction changes of the output of the HVAC controller. 
     
     
         10 . The method of  claim 1 , wherein the parameters comprise one or more of: a proportional gain, an integral gain, or a derivative gain. 
     
     
         11 . A method of tuning a heating, ventilation, and air conditioning (HVAC) controller to stabilize a setpoint signal received by the HVAC controller from an external HVAC controller, the method comprising:
 receiving data corresponding to an HVAC controller implementation environment, the data including information about the setpoint signal and feedback from an HVAC physical system controlled by the HVAC controller;   evaluating a reward function based on the data,   determining, based on the evaluating the reward function, one or more parameters for the HVAC controller; and   loading the parameters into the HVAC controller.   
     
     
         12 . The method of  claim 11 , wherein the reward function includes a term based on oscillations of the setpoint signal. 
     
     
         13 . The method of  claim 11 , wherein the reward function includes a term based on direction changes of the setpoint signal. 
     
     
         14 . The method of  claim 11 , wherein the reward function includes a term based on a stability of the setpoint signal. 
     
     
         15 . The method of  claim 14 , wherein the stability of the setpoint signal is based on a variation in the setpoint signal. 
     
     
         16 . The method of  claim 11 , wherein the loading the parameters comprises augmenting a control law of the HVAC controller such that the control law, after augmentation, comprises a first portion corresponding to other parameters and a second portion corresponding to the parameters. 
     
     
         17 . The method of  claim 11 , wherein loading the parameters comprises updating existing parameters of the HVAC controller. 
     
     
         18 . The method of  claim 11 , wherein the determining the parameters comprises using a reinforcement learning (RL), an online optimization-based, or an adaptive control technique. 
     
     
         19 . The method of  claim 11 , wherein the parameters comprise neural network weights. 
     
     
         20 . An offline optimization system configured to parameterize a heating, ventilation, and air-conditioning (HVAC) controller, the offline optimization system comprising:
 a processing unit configured to:
 receive one or more setpoint functions; 
 input the setpoint functions into a simulation of an HVAC controller environment; 
 evaluate an objective function on the simulation of the HVAC controller environment, the objective function being based on oscillations of an output of the HVAC controller in the simulation; 
 determine, based on the evaluation of the objective function, parameters for the HVAC controller; and 
 load the parameters into the HVAC controller.