Building thermal control techniques

Patent metadata

Field	Value
Publication number	US-2016146493-A1
Application number	US-201514985499-A
Country	US
Kind code	A1
Filing date	Dec 31, 2015
Priority date	Nov 26, 2014
Publication date	May 26, 2016
Grant date	—

Abstract

An algebra and differential equations model of a physical system is constructed based on available training data and physical system characteristics. A hybrid calibration process is carried out to iteratively calibrate both time-insensitive and time-sensitive parameters of the algebra and differential equations model so as to obtain parameter vectors. Vector auto-regression is applied to the parameter vectors to predict values of the parameters for a future time period.

First claim

What is claimed is:

1 . A method comprising the steps of:
constructing an algebra and differential equations model of a physical system based on available training data and physical system characteristics; carrying out a hybrid calibration process to iteratively calibrate both time-insensitive and time-sensitive parameters of said algebra and differential equations model so as to obtain parameter vectors; and applying vector auto-regression to said parameter vectors to predict values of said parameters for a future time period.

2 . The method of claim 1 , wherein, in said constructing step, said model of said physical system comprises a heat transfer model of a building, further comprising the additional steps of:
supplying a predictive model, based on said predicted values of said parameters for said future time period, to a building heating, ventilating, and air conditioning system model predictive controller; and controlling said building heating, ventilating, and air conditioning system in accordance with said predictive model.

3 . The method of claim 1 , wherein, in said constructing step, said model of said physical system comprises a heat transfer model of a building, further comprising the additional steps of:
supplying a predictive model, based on said predicted values of said parameters for said future time period, to a building heating, ventilating, and air conditioning system model predictive fault detection and diagnosis tool; and troubleshooting said building heating, ventilating, and air conditioning system in accordance with said predictive model.

4 . The method of claim 1 , wherein said step of carrying out said hybrid calibration process comprises:
for each sub time period of a predetermined time period of said available training data, training said time-insensitive and time-sensitive parameters together; based on said training of said time-insensitive and time-sensitive parameters together, training said time-insensitive parameters with said predetermined time period of said available training data, with said time sensitive parameters fixed, to obtain updated time-insensitive parameters; and using said updated time-insensitive parameters to train an updated set of time-sensitive parameters for each of said sub time periods.

5 . The method of claim 4 , further comprising repeating said steps of training said time-insensitive parameters with said predetermined time period of said available training data and using said updated time-insensitive parameters to train said updated set of time-sensitive parameters for each of said sub time periods, until convergence is achieved.

6 . The method of claim 5 , wherein:
said step of training said time-insensitive and time-sensitive parameters together for each sub time period of said predetermined time period of said available training data comprises:
making an initial estimate of said time-insensitive and time-sensitive parameters;
using a genetic algorithm to determine a parameter fitting of said time-insensitive and time-sensitive parameters for said each sub time period of said predetermined time period of said available training data; and
repeating said steps of making said initial estimate of said time-insensitive and time-sensitive parameters and using said genetic algorithm for said each sub time period until convergence is achieved;
said step of training said time-insensitive parameters to obtain updated time-insensitive parameters comprises:
making an initial estimate of said time insensitive parameters;
using said genetic algorithm to determine a parameter fitting of said time-insensitive parameters for said predetermined time period of said available training data; and
repeating said steps of making said initial estimate of said time insensitive parameters and using said genetic algorithm to determine said parameter fitting of said time-insensitive parameters until convergence is achieved; and
said step of using said updated time-insensitive parameters to train said updated set of time-sensitive parameters for each of said sub time periods comprises:
making an initial estimate of said time sensitive parameters;
using said genetic algorithm to determine a parameter fitting for said time sensitive parameters for said each sub time period of said predetermined time period of said available training data; and
repeating said steps of making said initial estimate of said time sensitive parameters and using said genetic algorithm for said time sensitive parameters until convergence is achieved.

7 . The method of claim 1 , wherein:
said step of carrying out said hybrid calibration process is implemented by a hybrid calibration sub-module of a modeling module, embodied on a non-transitory computer-readable medium, executing on at least one hardware processor; and said step of applying said vector auto-regression to said parameter vectors is implemented by a vector auto-regression sub-module of said modeling module, embodied on said non-transitory computer-readable medium, executing on said at least one hardware processor.

Assignees

• IBM

Inventors

• Ettl Markus R
• Lee Young M
• Yang Hongxia
• Zhang Rui

Classifications

• G05B15/02

  electric · 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

• G06F30/20

  Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) · CPC title

• G05B2219/2642

  Domotique, domestic, home control, automation, smart house · CPC title